Sphera Systematic Strategies Management Ltd Systematic strategies Systematic Trading – Where...

Sphera Systematic Strategies Management Ltd

Systematic strategies

Systematic Trading – Where Technology Makes Your Profit

Buky Carmeli - CEO [email protected]: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

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Sphera Systematic presents:


How to select an Algo-trading machine ?How to select an Algo-trading machine ?Evaluation from A to ZEvaluation from A to Z






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How to select an Algo-trading machine ?Evaluation from A to Z

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In this presentation

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o Glossary: “algo-trading”, “quantitative-trading” and “systematic-trading”, does it really matter?

o Algo-trading machine: communicating with multi-disciplines challenges

o “Black-box” evaluation of a given “algo-trading” machine

o Sphera algo-trading innovation program

o Conclusion

In this presentationIn this presentation

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About Sphera group


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$850M

o Over the past 11 years, Sphera increased its AUM, almost a year by year, from $70M (2004) to $850M (2015); Capital was raised from various investors, institutional, family offices & individuals (see graph)

About SpheraAbout Sphera

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o Over the said period, Sphera performed significantly better than Tel-Aviv-100 companies index and MSCI World index (see graph below)

o In 2013, one of our funds, Sphera Global Healthcare, ($500M by now) was ranked by Bloomberg Capital™ as one of the top 25 mid-size hedge funds, world wide

You may hit the <space> bar now in order to read moreYou may hit the <space> bar now in order to continue

o There are Hundreds of Millions of USDs in the Israeli market, seeking for unique, solid and stable financial alternatives; Sphera has a long term access to many institutional, family offices and individuals

o Our goal: Creating and marketing new investment vehicles: systematic-machine(s)-based fund(s)

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Glossary


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GlossaryGlossary

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o “Algo-trading” = “Algorithmic trading”; A trading machine that generates trading signals (i.e., decisions: “buy”, “sell”, “short”, “cover”, “add more”, “reduce”, “drop” etc.) based on a well predefined set of rules and criteria; In many cases, “algo-machine” means also that orders are sent, tracked and handled automatically (no middle man)

o “Quant-trading” = “Quantitative trading”; An “algo” machine that generates its trading signals based on quantified parameters which in many cases are meaningless for traditional economics

o “Systematic-trading” = all kinds and types of trading machines which generate their trading signals based on a well predefined set of rules and criteria; no discretionary decision is allowed !

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The algo-trading machines


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Fundamental

s of




o An algo-machine can be described as a collection of formulas; For example, F1 takes a set of input parameters and under certain conditions generates a trade signal …

o Where P1,P2,….Pn are various independent input parameters; For example P1=Price per share; P2=Volume acceleration; P3=High/Low…

It’s all about a formula…It’s all about a formula…

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F1(P1,P2,….Pn) Buy

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o An algo-machine can be described as a collection of formulas; For example, F1 takes a set of input parameters and under certain conditions generates a trade signal …

o … not that simple !

It’s all about a formula…It’s all about a formula…

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F2(P1,P2,….Pn) Sell

F3(P1,P2,….Pn) Short

F1(P1,P2,….Pn) Buy

F4(P1,P2,….Pn) Cover

Algo-tradingmachine

Algo-tradingmachine

P1

P2

P3

Pn

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o Historical datao Clean of errors and glitches

o Clean of gaps in price and volume

o Sufficient resolution

o Cost of data

o Independent parameterso As many as possible

o Learning set and Training seto Define “learning” and “training”

o Long enough but not too long

o Steady state

How to create a secret ?How to create a secret ?

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10:51 24.0510:52 24.0710:53 24.0810:54 24.0610:55 24.0510:56 24.0310:57 24.0210:58 24.0410:59 23.9011:00 24.0811:01 24.0911:02 24.1011:03 24.1111:04 24.1311:05 24.1411:06 24.1311:07 24.12

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o Clean of gaps in time, price or volume


o Cost of data




o Steady state


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10:51 24.0510:52 24.0710:53 24.0810:55 24.0510:56 24.0310:57 24.0210:58 24.0410:59 23.9011:00 24.0811:01 00.0011:02 24.1011:03 24.1111:04 24.1311:05 24.1411:06 24.1311:07 24.1211:08 24.11

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o Cost of data




o Steady state


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10:51 24.0510:52 24.0710:53 24.0810:54 24.0510:55 24.0310:56 24.0210:57 24.0410:58 24.0510:59 24.0611:00 24.0811:02 24.1011:03 24.1111:04 24.1311:05 24.1411:06 24.1311:07 24.1211:08 24.11

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o Cost of data




o Steady state


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10:51 24.0510:52 24.0710:53 24.0810:54 24.0510:55 24.0310:56 24.0210:57 24.0410:58 24.0510:59 24.0611:00 24.0811:02 24.1011:03 24.1111:04 24.1311:05 24.1411:06 24.1311:07 24.1211:08 24.11

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o Clean of gaps in time price or volume


o Cost of data




o Steady state


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F1(P1,P2,….Pn) Action

Lack of parameters or relying on correlated parameters will yield destructive results !

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o Cost of data


o Learning set and Training set)s)o Define “learning” and “training”


o Steady state


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Learning

Training

Develop your secret(s); Search for statistical pattern(s)Check your findings; Test your secret formulaAlways use data that has never been used (seen) before

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o Cost of data


o Learning set and Training set(s)o Define “learning” and “training”

o Long historical data (T >>> L)

o Far history vs. near present

o Over fitting


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Learning

Training

L

T

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o Get real time data from marketo Use broker in order to send & track orders

Go to market…Go to market…

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Algo machine

Algo machine

MarketMarketData

providerData

provider

BrokerBrokerP1,P2,…Pn

Buy, Sell, Short, Cover,

Add, Reduce…

CostAccuracy

Delay

Detailed

CostAccuracy

Delay

DetailedCostDelay

CostDelay

o Jump into cold water…

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o The broker may divide the original order among various brokerage firms


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Algo machine

Algo machine

MarketMarketData

providerData

provider

P1,P2,…Pn


Add, Reduce…

CostAccuracy

Delay

Detailed

CostAccuracy

Delay

Detailed

BrokerBroker

BrokerBroker

BrokerBroker

BrokerBroker

SplitReturn price

Availability

SplitReturn price

Availability

Return price vs. Historical data

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o Transparency? Please meet the “dark pool” technology


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Algo machine

Algo machine

MarketMarketData

providerData

provider

P1,P2,…Pn


Add, Reduce…

CostAccuracy

Delay

Detailed

CostAccuracy

Delay

Detailed

BrokerBroker

BrokerBroker

BrokerBroker

BrokerBroker

Dark poolDark pool

CostNot for everyone

OTC-style (undisclosed)CostNot for everyone

OTC-style (undisclosed)

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MarketMarket

o What do you mean by “market” ?


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Algo machine

Algo machine

MarketData

providerData

provider



Add, Reduce…

CostAccuracy

Delay

Detailed

CostAccuracy

Delay

Detailed

MarketMarketMarketMarket

MarketMarket

MarketMarket

MarketMarketSplitReturn price

Availability

SplitReturn price

Availability

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MarketMarket

o It’s not just about trading signals …. You need to

know how to route your orders…


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Algo machine

Algo machine

MarketData

providerData

provider



Add, Reduce…

CostAccuracy

Delay

Detailed

CostAccuracy

Delay

Detailed


MarketMarket MarketMarket

MarketMarket

ExecutionalgorithmExecutionalgorithm

BrokerBroker

BrokerBroker

BrokerBroker

Dark poolDark pool

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• Split & Return price• % of filling• # of Opportunities

Algo-machine performanceAlgo-machine performance

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ROI (%)

Size of Investment

ROI vs. Investment

1 2 3

• Commission• Extra payments• Indirect cost of operation

Stabilization point

Stabilization point

Saturation point

Saturation point

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ROI (%)

Size of Investment

1 2 3

ROI vs. Investment

Working area

• While area-1 is not profitable, area-3 is considered as “unstable area”

• A stable algo-machine should “stay” in the “blue” area (the “working area”)

Max. InvestmentScalability

Sphera’s scope of interest: > $50MSphera’s investment policy: < 25% of AUM

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ROI (%)

Size of Investment

2

ROI vs. time

Selected working

pointROI-Y

Investment-X

• If we work inside the “working area” we should be able to achieve a given ROI per a given investment; Well ….

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ROI (%)

Time

ROI vs. time

ROI-Y

Investment-X

• Observation: ROI decay over time• Explanation: The learning and training sets do not

represent the current market situation • Solution: Built-in or external (forced) calibration

Algo machine

Market

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MarketMarket

o It’s not just about trading signals …. You need to

know how to route your orders…

Another building block…Another building block…

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Algo machine

Algo machine

Market



Add, Reduce…


MarketMarket MarketMarket

MarketMarket

Execution algorithmExecution algorithm Dark

poolDark pool

Calibration algorithmCalibration algorithm

Data provider

Data provider

CostAccuracy

Delay

Detailed

CostAccuracy

Delay

Detailed BrokerBroker

BrokerBroker

BrokerBroker

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A full algo-machineA full algo-machine

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Signalsalgorithm

Signalsalgorithm

Generation of trading

signals

Generation of trading

signals

ExecutionalgorithmExecutionalgorithm

Control of orders routing

Control of orders routing

CalibrationalgorithmCalibrationalgorithm

Decay detections

& parametric calibration

Decay detections

& parametric calibration

AllocationalgorithmAllocationalgorithm

Budget allocation

Budget allocation

Risk control

algorithm

Risk control

algorithm

Leverage & hedging

algorithm

Leverage & hedging

algorithm

Datacollection

system

Datacollection

system

Collection of real time

data

Collection of real time

data

RecoveryalgorithmRecoveryalgorithm

Survival and

recovery of orders

Survival and

recovery of orders

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Black-Box evaluation


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o The challenge: evaluate future “behavior” of a given algo-trading machine without using its secret formula…

o The key data we need: log of real-money trades (easily provided by the brokerage firm)

o Each log row describes a certain executed order:

o Split orders will be reported in two separated rows:

Black-box evaluationBlack-box evaluation

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Date Time Ticker Action Price Quantity Commission


“ Time “ “ Price Quantity Commission

05/11/11 10:33:05 ABCD BUY $12.05 3000 $2.50

05/11/11 10:33:05 ABCD BUY $12.05 1800 $2.50

05/11/11 10:33:06 ABCD BUY $12.07 1200 0

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o By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . .


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o By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . .o Win/Lose ratio


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Losing trades, organized from min.

loss to max. loss

Winning trades, organized from max.

gain to min. gain

Maximum loss

Maximum gain# of W

# of L

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# of W# of L = > 1, < 2

W areaL area

>> 2

70% * W area70% * L area

>= 2

70%

70%

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o Budget utilization

o Leverage control


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Time

Investment Av. Investment

Max. Investment70% of trades

70%

70%

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o Leverage control

o Price sensitivity

o Assets distribution




70%

70%Av. Gain in $

Av. Loss in $

Symbol1

Symbol2

Symbol3

Symbol4

Symbol5

Symbol6

Symbol7

Symbol8

PPS

Volume

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o Leverage control

o Price sensitivity


o Exposure Level

o Market correlation


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Time

InvestmentLong

Short

Time

Trades100%

50%

Long

Short

Market

Market

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o Leverage control

o Price sensitivity


o Exposure Level

o Market correlation

o Performance stability


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Window size

ROI

10% 50% 100%

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o The main objective of the black box evaluation is to estimate how wide is the “blue” area, how “far” the maximum investment point is “located”, and how slow this “blue line” moves from the “stability point” down to the “saturation point”


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SSIPSphera Systematic Innovation

Program


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• Sphera group is focusing on managing of hedge funds and financial products; NOT in developing of algo-trading machines

• We are NOT a Venture Capital (VC) fund; Sphera Systematic is NOT willing to take any managerial position in any algo-trading venture; We have NO intention to invest in the algo-trading venture or take any kind of holdings of ownership

• We are NOT interested in exploring the inside secrets or IP of the algo-trading machine

• We ask for no exclusivity

SSIP

Sphera Systematic Innovation Program

SSIP


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• We are seeking for good financial products, i.e., algo-trading machines, that meet the following requirements: Operational time: >= 12 months Av. actual exposure: > $500M @ Leverage < 1.5 Budget utilization: > 70% @ 80% of time Potential capacity: > $50M (potential or actual) Potential Annual ROI : >= 8% - 10% @ $50M Max. monthly Draw-down: <= 4% Av. monthly Draw-down: <= 2% Win/Lose trades ratio: > 1, < 2 Gain/Loss trades ratio: >> 2, > 2 @ 70% of trades Best trade/Worst trade: > 3

SSIP


SSIP


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SSIP


SSIP


NDA

DDQ

Report analysis

Wet test

Fund agreement

Go… Sphera groupSphera group

Sphera systematic• Sign mutual NDA doc• Protect entrepreneur’s

secrets, IPs etc.

• Due Diligence Questionnaire

• Hundreds of questions• Persistency• Consistency

• Software tools• Simulate trade by trade• Investment committee• Add small amount• Analyze actual

performance

• Investment manager• Fund manager• Best execution• Cross Trading• Reporting• … more

Not less than 6 months

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Conclusion


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ConclusionConclusion

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o “Algo-machine” is a very complicated machine; It requires deep understanding in wide range of areas: mathematics, statistics, computing, engineering, capital market structure, trading mechanism … and we didn’t mention yet funds raising, fund management and more; The founder must reflect strong personality

o The “algo-trading” technology is NOT a replacement to traditional trading style; Its another way to utilize the modern technology in order to enhance the capital market liquidity and create more trading opportunities

o Sphera group, the largest Israeli HF management group, will announce its first systematic-based financial product within a few months; Some building block of this product are Israeli ventures that have been explored over the past 6-7 months

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