Sphera Systematic presents:

Please Do NOT distribute this presentation Sphera Systematic presents: How to select an Algo-trading machine ? Evaluation from A to Z Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation Sphera Systematic presents: How to select an Algo-trading machine ? Evaluation from A to Z Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel 01

Please Do NOT distribute this presentation In this presentation… Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation In this presentation • Glossary: “algo-trading”, “quantitative-trading” and “systematic-trading”, does it really matter? • Algo-trading machine: communicating with multi-disciplines challenges • “Black-box” evaluation of a given “algo-trading” machine • Sphera algo-trading innovation program • Conclusion 02 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation About Sphera group Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation About Sphera Sphera AUM in USD, 2004-2014 Sphera Tel-Aviv Index MSCI World $850M • 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) 6 • Over the said period, Sphera performed significantly betterthan Tel-Aviv-100 companies index and MSCI World index (see graph below) • 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 • 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 • Our goal:Creating and marketing new investment vehicles: systematic-machine(s)-based fund(s) You may hit the <space> bar now in order to read more 03 You may hit the <space> bar now in order to continue You may hit the <space> bar now in order to read more

Please Do NOT distribute this presentation Glossary Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation Glossary • “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 handledautomatically (no middle man) • “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 • “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 ! 04 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation The algo-trading machines Fundamentals of Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation It’s all about a formula… F1(P1,P2,….Pn) Buy • 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 … • Where P1,P2,….Pn are various independent input parameters; For example P1=Price per share; P2=Volume acceleration; P3=High/Low… 05 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation It’s all about a formula… F1(P1,P2,….Pn) F2(P1,P2,….Pn) F3(P1,P2,….Pn) F4(P1,P2,….Pn) Short Cover Sell Buy Algo-trading machine P1 P2 P3 Pn • 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 … • … not that simple ! 06 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation How to create a secret ? 10:51 24.05 10:52 24.07 10:53 24.08 10:54 24.06 10:55 24.05 10:56 24.03 10:57 24.02 10:58 24.04 10:59 23.90 11:00 24.08 11:01 24.09 11:02 24.10 11:03 24.11 11:04 24.13 11:05 24.14 11:06 24.13 11:07 24.12 • Historical data • Clean of errors and glitches • Clean of gaps in price and volume • Sufficient resolution • Cost of data • Independent parameters • As many as possible • Learning set and Training set • Define “learning” and “training” • Long enough but not too long • Steady state 07 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation How to create a secret ? 10:51 24.05 10:52 24.07 10:53 24.08 10:55 24.05 10:56 24.03 10:57 24.02 10:58 24.04 10:59 23.90 11:00 24.08 11:01 00.00 11:02 24.10 11:03 24.11 11:04 24.13 11:05 24.14 11:06 24.13 11:07 24.12 11:08 24.11 • Historical data • Clean of errors and glitches • Clean of gaps in time, price or volume • Sufficient resolution • Cost of data • Independent parameters • As many as possible • Learning set and Training set • Define “learning” and “training” • Long enough but not too long • Steady state 07 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation How to create a secret ? 10:51 24.05 10:52 24.07 10:53 24.08 10:54 24.05 10:55 24.03 10:56 24.02 10:57 24.04 10:58 24.05 10:59 24.06 11:00 24.08 11:02 24.10 11:03 24.11 11:04 24.13 11:05 24.14 11:06 24.13 11:07 24.12 11:08 24.11 • Historical data • Clean of errors and glitches • Clean of gaps in time, price or volume • Sufficient resolution • Cost of data • Independent parameters • As many as possible • Learning set and Training set • Define “learning” and “training” • Long enough but not too long • Steady state 07 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation How to create a secret ? F1(P1,P2,….Pn) F1(P1,P2,….Pn,Pn+1) Action Action Lack of parameters or relying on correlated parameters will yield destructive results ! • Historical data • Clean of errors and glitches • Clean of gaps in time price or volume • Sufficient resolution • Cost of data • Independent parameters • As many as possible • Learning set and Training set • Define “learning” and “training” • Long enough but not too long • Steady state 07 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation How to create a secret ? Learning Training Develop your secret(s); Search for statistical pattern(s) Check your findings; Test your secret formula Always use data that has never been used (seen) before • Historical data • Clean of errors and glitches • Clean of gaps in time price or volume • Sufficient resolution • Cost of data • Independent parameters • As many as possible • Learning set and Training set)s) • Define “learning” and “training” • Long enough but not too long • Steady state 07 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation How to create a secret ? Learning L Training T • Historical data • Clean of errors and glitches • Clean of gaps in time price or volume • Sufficient resolution • Cost of data • Independent parameters • As many as possible • Learning set and Training set(s) • Define “learning” and “training” • Long historical data (T >>> L) • Far history vs. near present • Over fitting 07 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Go to market… • Jump into cold water… Cost Accuracy Delay Detailed Market Data provider Cost Delay Algo machine Broker P1,P2,…Pn Buy, Sell, Short, Cover, Add, Reduce… • Get real time data from market • Use broker in order to send & track orders 08 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Go to market… Broker Broker Cost Accuracy Delay Detailed Market Data provider Broker Split Return price Availability Algo machine Broker P1,P2,…Pn Buy, Sell, Short, Cover, Add, Reduce… Return price vs. Historical data • The broker may divide the original order among various brokerage firms You may hit the <space> bar now in order to continue 08

Please Do NOT distribute this presentation Go to market… Broker Broker Cost Accuracy Delay Detailed Market Data provider Broker Cost Not for everyone OTC-style (undisclosed) Algo machine Broker Dark pool P1,P2,…Pn Buy, Sell, Short, Cover, Add, Reduce… • Transparency? Please meet the “dark pool” technology 08 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Go to market… Market Market Market Cost Accuracy Delay Detailed Market Market Data provider Split Return price Availability Market Market Algo machine Broker P1,P2,…Pn Buy, Sell, Short, Cover, Add, Reduce… • What do you mean by “market” ? 08 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Go to market… Broker Market Broker Market Market Cost Accuracy Delay Detailed Market Market Data provider Broker Market Market Algo machine Broker Execution algorithm Dark pool P1,P2,…Pn Buy, Sell, Short, Cover, Add, Reduce… • It’s not just about trading signals …. You need to know how to route your orders… 08 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Algo-machine performance ROI vs. Investment ROI (%) • Commission • Extra payments • Indirect cost of operation Stabilization point Saturation point • Split & Return price • % of filling • # of Opportunities Size of Investment 3 1 2 09 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Algo-machine performance ROI vs. Investment • 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”) ROI (%) Max. Investment Scalability Working area Size of Investment 3 1 2 Sphera’s scope of interest: > $50M Sphera’s investment policy: < 25% of AUM 09 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Algo-machine performance ROI vs. time • If we work inside the “working area” we should be able to achieve a given ROI per a given investment; Well …. ROI (%) Selected working point ROI-Y Investment-X Size of Investment 2 09 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Algo-machine performance ROI vs. time Investment-X ROI (%) Market Algo machine ROI-Y Time • 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 10 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Another building block… Broker Market Broker Market Market Cost Accuracy Delay Detailed Market Market Data provider Broker Market Market Algo machine Broker Execution algorithm Dark pool P1,P2,…Pn Buy, Sell, Short, Cover, Add, Reduce… Calibration algorithm • It’s not just about trading signals …. You need to know how to route your orders… 11 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation A full algo-machine Recovery algorithm Data collection system Risk control algorithm Calibration algorithm Allocation algorithm Execution algorithm Signals algorithm Survival and recovery of orders Generation of trading signals Control of orders routing Decay detections & parametric calibration Budget allocation Leverage & hedging algorithm Collection of real time data 12 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-Box evaluation Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation Black-box evaluation Date Time Ticker Action Price Quantity Commission 05/11/11 05/11/11 10:33:05 10:33:05 ABCD ABCD BUY BUY $12.05 $12.05 3000 1800 $2.50 $2.50 05/11/11 10:33:06 ABCD BUY $12.07 1200 0 Date Time Ticker Action Price Quantity Commission “ Time “ “ Price Quantity Commission • The challenge:evaluate future “behavior” of a given algo-trading machine without using its secret formula… • The key data we need: log of real-money trades (easily provided by the brokerage firm) • Each log row describes a certain executed order: • Split orders will be reported in two separated rows: 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission Date Time Ticker Action Price Quantity Commission • By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . . 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation Losing trades, organized from min. loss to max. loss Maximum loss # of L # of W Maximum gain Winning trades, organized from max. gain to min. gain • By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . . • Win/Lose ratio 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation 70% 70% # of W # of L = > 1, < 2 W area L area 70% * W area 70% * L area >> 2 >= 2 • By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . . • Win/Lose ratio 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation 70% of trades Max. Investment Av. Investment Investment 70% 70% Time • By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . . • Win/Lose ratio • Budget utilization • Leverage control 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation PPS Av. Gain in $ 70% Av. Loss in $ Volume 70% Symbol 1 Symbol 2 Symbol 3 Symbol 4 Symbol 5 Symbol 6 Symbol 7 Symbol 8 • By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . . • Win/Lose ratio • Budget utilization • Leverage control • Price sensitivity • Assets distribution 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation Investment Market Long Short Time Trades 100% Long Market 50% Short Time • By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . . • Win/Lose ratio • Budget utilization • Leverage control • Price sensitivity • Assets distribution • Exposure Level • Market correlation 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation ROI Window size 10% 50% 100% • By report analysis, a trade-by-trade simulation, we can inspect critical parameters such as . . . • Win/Lose ratio • Budget utilization • Leverage control • Price sensitivity • Assets distribution • Exposure Level • Market correlation • Performance stability 13 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Black-box evaluation • 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” 14 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation SSIP Sphera Systematic Innovation Program Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation SSIPSphera Systematic Innovation Program We are NOT … 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 15 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation SSIPSphera Systematic Innovation Program • 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 16 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation SSIPSphera Systematic Innovation Program Sphera systematic • Investment committee • Add small amount • Analyze actual performance • Software tools • Simulate trade by trade • Investment manager • Fund manager • Best execution • Cross Trading • Reporting • … more • Sign mutual NDA doc • Protect entrepreneur’s secrets, IPs etc. • Due Diligence Questionnaire • Hundreds of questions • Persistency • Consistency NDA DDQ Report analysis Wet test Fund agreement Sphera group Go… Not less than 6 months 17 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Conclusion Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Please Do NOT distribute this presentation Conclusion • “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 • 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 • Sphera group, the largest Israeli HF management group, will announce its first systematic-based financial product within a few months; Some building blockof this product are Israeli ventures that have been explored over the past 6-7 months 18 You may hit the <space> bar now in order to continue

Please Do NOT distribute this presentation Thank you תודה למערכת "אנשים ומחשבים"ולבורסה לניירות ערךעל שנרתמו לארגן את כנס האלגו-טריידינג הראשון בישראל; תודה מיוחדת ואישית בשם קבוצת "ספרה" לפלי הנמר, לנטלי, ליהודהולטולי שפעלה במרץ וללא לאות לקיום כנס זה Buky Carmeli - CEO Buky@spherafund.comCell: +972(54)930-6157 / 21 Ha’arba’ah st. Tel Aviv, Israel

Sphera Systematic presents: