Finding the Right Product viaFinding the Right Product via
RealReal--time Intelligent Internettime Intelligent Internet
Se...
Example: TurboExample: Turbo--charging Technicalcharging Technical
Analysis with realAnalysis with real--time Internettime...
Introductory RemarksIntroductory Remarks
The following slides illustrate the use of an Intelligent InternThe following sli...
Introductory RemarksIntroductory Remarks
Readers may require some basic knowledge ofReaders may require some basic knowled...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
Successful trading involves discipline andSuccessful trading ...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
Some traditional ways of using technicalSome traditional ways...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
However, these methods go back a long time:However, these met...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
While there is truth in not trying to fix somethingWhile ther...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
In all these ways, there is still a need for traderIn all the...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
Information technology has made the greatestInformation techn...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
The premise is that, with so much dataThe premise is that, wi...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
One way to is to do DataOne way to is to do Data--MiningMinin...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
Use Data Mining to limit oneUse Data Mining to limit one’’ss
...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
What exactly is DataWhat exactly is Data--Mining the streamin...
DataData--MiningMining –– TurboTurbo--charging TAcharging TA
More Examples:More Examples:
what kind of conditions would be...
DataData--MiningMining –– InterpretingInterpreting
Context/EnvironmentContext/Environment
Applications of DataApplications...
DataData--MiningMining –– InterpretingInterpreting
Context/EnvironmentContext/Environment
We need to understandWe need to ...
DataData--MiningMining –– InterpretingInterpreting
Context/EnvironmentContext/Environment
Examples of good dataExamples of...
Interpreting Context/EnvironmentInterpreting Context/Environment
Uncorrelated Stocks 1Uncorrelated Stocks 1
DataData--MiningMining –– InterpretingInterpreting
Context/EnvironmentContext/Environment
Knowing what stocks are correlat...
DataData--MiningMining –– InterpretingInterpreting
Context/EnvironmentContext/Environment
More on Environmental Issues:Mor...
Interpreting Context/EnvironmentInterpreting Context/Environment
Sector performanceSector performance
DataData--MiningMining –– InterpretingInterpreting
Context/EnvironmentContext/Environment
More on Environmental Issues:Mor...
Interpreting Context/EnvironmentInterpreting Context/Environment
Individual stock riskIndividual stock risk--returnreturn
DataData--MiningMining –– InterpretingInterpreting
Context/EnvironmentContext/Environment
More on Environmental Issues:Mor...
DataData--MiningMining –– Interpreting RiskInterpreting Risk
Interpreting Risk:Interpreting Risk:
Talking about risk, woul...
Interpreting RiskInterpreting Risk
Individual stock riskIndividual stock risk
Interpreting RiskInterpreting Risk
Risk of stock inside sectorRisk of stock inside sector
Interpreting RiskInterpreting Risk
Risk reportsRisk reports
DataData--MiningMining –– Analytical SupportAnalytical Support
Analytical Support:Analytical Support:
Many things are poss...
DataData--MiningMining –– Analytical SupportAnalytical Support
Consistency in PerformanceConsistency in Performance
DataData--MiningMining –– Analytical SupportAnalytical Support
Another ExampleAnother Example
For a stock (STAR) that has ...
DataData--MiningMining –– Analytical SupportAnalytical Support
Consistent PerformanceConsistent Performance
DataData--MiningMining –– Analytical SupportAnalytical Support
Another Example:Another Example:
For a stock (TRW) that has...
DataData--MiningMining –– Analytical SupportAnalytical Support
Summarizing:Summarizing:
One tentative conclusion is that b...
DataData--MiningMining –– Analytical SupportAnalytical Support
AnotherAnother egeg –– Parametric OptimizationParametric Op...
DataData--MiningMining –– Analytical SupportAnalytical Support
This logic of using data mining to support technicalThis lo...
DataData--MiningMining –– VisualizationVisualization
DataData--Mining is not just about number crunching. ItMining is not ...
DataData--MiningMining –– VisualizationVisualization
InIn the next slide, we will display a way ofthe next slide, we will ...
VisualizationVisualization -- HeatMapsHeatMaps
NasdaqNasdaq Market, May 13, 02Market, May 13, 02
VisualizationVisualization –– Heat MapsHeat Maps
The previousThe previous HeatMapHeatMap shows theshows the NasdaqNasdaq m...
VisualizationVisualization –– Heat MapsHeat Maps
NasdaqNasdaq Market, Jul 19, 02Market, Jul 19, 02
VisualizationVisualization –– Heat MapsHeat Maps
That was theThat was the HeatMapHeatMap of theof the NasdaqNasdaq on 19 J...
DataData--MiningMining -- VisualizationVisualization
Visualization can be done on anything toVisualization can be done on ...
VisualizationVisualization –– Price Volume ChartsPrice Volume Charts
PV Chart, EPV Chart, E--bay, Jul 22, 02bay, Jul 22, 02
DataData--MiningMining –– Developing FocusDeveloping Focus
Some examples:Some examples:
If you wish to use a particular te...
DataData--MiningMining –– Developing FocusDeveloping Focus
Best Stock ScanBest Stock Scan
DataData--MiningMining –– Developing FocusDeveloping Focus
Best Indicator ScanBest Indicator Scan
DataData--MiningMining –– Developing FocusDeveloping Focus
In indicator analysis, with typical American abundance,In indic...
DataData--MiningMining –– Developing FocusDeveloping Focus
Indicator summaryIndicator summary
DataData--MiningMining –– Developing FocusDeveloping Focus
The slide shows howThe slide shows how
datadata--mining can bem...
DataData--MiningMining –– Developing FocusDeveloping Focus
Example 8:Example 8:
How about eliminatingHow about eliminating...
DataData--MiningMining –– Putting it togetherPutting it together
DataData--mining can be used on its own, butmining can be...
RealReal--time Backtime Back--testingtesting
HistoricalHistorical BacktestingBacktesting
BackBack--testing: Portfolio Diversificationtesting: Portfolio Diversification
Risk ManagementRisk Management -- tradingtr...
It’s the whole
DataData--Mining:Mining: GooglingGoogling the Streamingthe Streaming
PricesPrices
The Essence ofThe Essence...
Thank YouThank You
Guan Seng Khoo, PhDGuan Seng Khoo, PhD
Head, Global Risk (Models Validation)Head, Global Risk (Models V...
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt
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Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt

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Pre-Big Data Analytics (Algorithmic Google on Streaming Prices):
Analytical tools which perform real-time analysis for trading/investment decisions, catered to different types of investors, incl. day-trading, swing-trading & system trading. Think of it as a convenience store ("7-Eleven") of real-time trading ideas

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Algorithmic Google on Streaming Prices _Technical&FundamentalAnalyses + PortfolioMgt

  1. 1. Finding the Right Product viaFinding the Right Product via RealReal--time Intelligent Internettime Intelligent Internet Search Services Solutions & BISearch Services Solutions & BI Tools on Streaming PricesTools on Streaming Prices FOCUS ON FINANCIAL ANALYTICS & INFOFOCUS ON FINANCIAL ANALYTICS & INFO-- UTILITIES SOFTWARE APPLICATIONUTILITIES SOFTWARE APPLICATION By Guan Seng Khoo*, PhDBy Guan Seng Khoo*, PhD Online infoOnline info--analytics, BI and CRM tools catering to allanalytics, BI and CRM tools catering to all clients that trade at an institutional/retail siteclients that trade at an institutional/retail site Email:Email: Khoo.GuanKhoo.Guan--Seng@standardchartered.comSeng@standardchartered.com gskhoo@gmail.comgskhoo@gmail.com
  2. 2. Example: TurboExample: Turbo--charging Technicalcharging Technical Analysis with realAnalysis with real--time Internettime Internet Search Tools on Streaming PricesSearch Tools on Streaming Prices ExEx--SVP, American Bourses Corporation*SVP, American Bourses Corporation* In partnership with Townsend Analytics & TerraIn partnership with Townsend Analytics & Terra Nova Trading, ChicagoNova Trading, Chicago (*Spin(*Spin--off co. of the Man Group)off co. of the Man Group) *Headed a team of financial engineers &*Headed a team of financial engineers & quantsquants that designed & managed the Manthat designed & managed the Man-- DrapeauDrapeau Response Fund, an algorithmic hedgeResponse Fund, an algorithmic hedge fund in the 90sfund in the 90s
  3. 3. Introductory RemarksIntroductory Remarks The following slides illustrate the use of an Intelligent InternThe following slides illustrate the use of an Intelligent Internet Searchet Search Engine with Tools applied on Streaming Prices (realEngine with Tools applied on Streaming Prices (real--time) for thetime) for the Financial MarketsFinancial Markets For identifying instruments that meet the userFor identifying instruments that meet the user’’s criteria in terms ofs criteria in terms of CapitalCapital RiskRisk investment horizoninvestment horizon etc.etc. Allows users to study the consistency of individual stockAllows users to study the consistency of individual stock’’s (or assets (or asset classclass’’) historical risk) historical risk--return performance.return performance. Allows users to compare the historical performance of selectedAllows users to compare the historical performance of selected instrumentsinstruments For analyzing the historical riskFor analyzing the historical risk--return performance of various assetreturn performance of various asset classes.classes. e.g., based on comparative study of the riske.g., based on comparative study of the risk--return performancereturn performance ofof securities that fall within the same category, e.g. Banking sectsecurities that fall within the same category, e.g. Banking sector.or.
  4. 4. Introductory RemarksIntroductory Remarks Readers may require some basic knowledge ofReaders may require some basic knowledge of trading and investment to appreciate thetrading and investment to appreciate the contentscontents Previously in 2000Previously in 2000--2002, we2002, we overlayedoverlayed ourour webweb--based search tools onbased search tools on RealTickRealTick, the, the intelligent trading and orderintelligent trading and order--routing system,routing system, owned by Townsend Analytics (Chicago)owned by Townsend Analytics (Chicago) Thank YouThank You
  5. 5. DataData--MiningMining –– TurboTurbo--charging TAcharging TA Successful trading involves discipline andSuccessful trading involves discipline and rigorous methods of analyses:rigorous methods of analyses: FundamentalsFundamentals TechnicalsTechnicals Dynamic Asset AllocationDynamic Asset Allocation DiversificationDiversification
  6. 6. DataData--MiningMining –– TurboTurbo--charging TAcharging TA Some traditional ways of using technicalSome traditional ways of using technical analysis include:analysis include: Visual AnalysisVisual Analysis Indicator AnalysisIndicator Analysis Historical BackHistorical Back--testingtesting These continue to be useful and form the coreThese continue to be useful and form the core body of knowledge and practice among technicalbody of knowledge and practice among technical traderstraders
  7. 7. DataData--MiningMining –– TurboTurbo--charging TAcharging TA However, these methods go back a long time:However, these methods go back a long time: Visual Technical AnalysisVisual Technical Analysis –– Edwards and Magee haveEdwards and Magee have been around since the 1920s and we have just reachedbeen around since the 1920s and we have just reached the umpteenth edition that looks essentially the same.the umpteenth edition that looks essentially the same. Computers plot the charts but the tools are the same.Computers plot the charts but the tools are the same. Indicators still use the same algorithms that were onceIndicators still use the same algorithms that were once calculated by simple calculators such as Wildercalculated by simple calculators such as Wilder’’s RSI.s RSI. Historical backHistorical back--testing, from the days oftesting, from the days of ComputracComputrac andand TeletracTeletrac, still crunch data the same old way : brute, still crunch data the same old way : brute--forceforce testing of technical indicators.testing of technical indicators.
  8. 8. DataData--MiningMining –– TurboTurbo--charging TAcharging TA While there is truth in not trying to fix somethingWhile there is truth in not trying to fix something thatthat ““ainain’’tt brokebroke””, it is valid to ask, is it possible to, it is valid to ask, is it possible to make technical analysis better?make technical analysis better? Some interesting questions:Some interesting questions: Can we do large scale screening of stocks before weCan we do large scale screening of stocks before we interpret their chart patterns?interpret their chart patterns? Can we improve some indicators to make better use ofCan we improve some indicators to make better use of computer technologies?computer technologies? Can we use better dataCan we use better data--mining techniques to improvemining techniques to improve onon backtestingbacktesting??
  9. 9. DataData--MiningMining –– TurboTurbo--charging TAcharging TA In all these ways, there is still a need for traderIn all these ways, there is still a need for trader decision makingdecision making –– trader input is always requiredtrader input is always required for good trading results to be attainable.for good trading results to be attainable. While there may be some better systems thanWhile there may be some better systems than others, there is nothing out there which has aothers, there is nothing out there which has a perfect track record. There is no Holy Grail orperfect track record. There is no Holy Grail or silver bullet.silver bullet. But we can, using modern computer techniques,But we can, using modern computer techniques, turboturbo--charge traditional technical analysis.charge traditional technical analysis.
  10. 10. DataData--MiningMining –– TurboTurbo--charging TAcharging TA Information technology has made the greatestInformation technology has made the greatest advanceadvance collecting datacollecting data.. Having data is very importantHaving data is very important -- the creation ofthe creation of raw data is the equivalent of having discoveredraw data is the equivalent of having discovered ““therethere’’s gold in them hillss gold in them hills””. Now, we have. Now, we have literally mountains of data (which is whatliterally mountains of data (which is what Google does best on static data).Google does best on static data). We have to use the appropriate tools to mineWe have to use the appropriate tools to mine and find the nuggetsand find the nuggets
  11. 11. DataData--MiningMining –– TurboTurbo--charging TAcharging TA The premise is that, with so much dataThe premise is that, with so much data (27,000 publicly traded US securities in 2002,(27,000 publicly traded US securities in 2002, in every barin every bar--size conceivable)size conceivable) collectedcollected, it is, it is possible to use datapossible to use data--mining (scanning,mining (scanning, screening etc) to narrow down to ascreening etc) to narrow down to a reasonable number of situations, whetherreasonable number of situations, whether these are securities or bar sizes, to make itthese are securities or bar sizes, to make it easier to apply traditional techniques,easier to apply traditional techniques, analyzeanalyze them correctly and make a goodthem correctly and make a good trade.trade.
  12. 12. DataData--MiningMining –– TurboTurbo--charging TAcharging TA One way to is to do DataOne way to is to do Data--MiningMining DataData--Mining has been called by various lessMining has been called by various less intimidating names :intimidating names : ScreeningScreening ScanningScanning PreprocessingPreprocessing MappingMapping VisualizingVisualizing
  13. 13. DataData--MiningMining –– TurboTurbo--charging TAcharging TA Use Data Mining to limit oneUse Data Mining to limit one’’ss trading universe, so that it is possibletrading universe, so that it is possible to apply technical analysis fruitfully,to apply technical analysis fruitfully, AND yet retain the flexibility to shiftAND yet retain the flexibility to shift focus to where the action is.focus to where the action is.
  14. 14. DataData--MiningMining –– TurboTurbo--charging TAcharging TA What exactly is DataWhat exactly is Data--Mining the streaming pricesMining the streaming prices ala Google?ala Google? In our context, it is the study of historical data toIn our context, it is the study of historical data to understand relationships among instruments andunderstand relationships among instruments and their environment.their environment. Examples:Examples: what kind of price and volatility behavior does awhat kind of price and volatility behavior does a particular instrument normally have?particular instrument normally have? How does it normally trade at different times of day,How does it normally trade at different times of day, days of week, or seasons of year etc?days of week, or seasons of year etc? How instruments are correlated with others?How instruments are correlated with others? How does a stock behave within its sector?How does a stock behave within its sector?
  15. 15. DataData--MiningMining –– TurboTurbo--charging TAcharging TA More Examples:More Examples: what kind of conditions would be mostwhat kind of conditions would be most appropriate for the successful application ofappropriate for the successful application of Moving Averages? Or Oscillators?Moving Averages? Or Oscillators? What time bar tends to be useful for trading aWhat time bar tends to be useful for trading a particular stock?particular stock? Show data in maps and picturesShow data in maps and pictures
  16. 16. DataData--MiningMining –– InterpretingInterpreting Context/EnvironmentContext/Environment Applications of DataApplications of Data--Mining in TA:Mining in TA: Interpreting Context/EnvironmentInterpreting Context/Environment Interpreting RiskInterpreting Risk Analytical SupportAnalytical Support VisualizationVisualization Narrowing ChoicesNarrowing Choices
  17. 17. DataData--MiningMining –– InterpretingInterpreting Context/EnvironmentContext/Environment We need to understandWe need to understand ““contextcontext”” oror ““environmentenvironment”” in order that we do the right thingsin order that we do the right things in the trading process.in the trading process. With that said, it sounds dangerously close to whatWith that said, it sounds dangerously close to what people have calledpeople have called ““curve fittingcurve fitting”” It is possible that a pedantic approach to dataIt is possible that a pedantic approach to data-- mining will glean nothing from history to apply tomining will glean nothing from history to apply to our benefit for trading the future.our benefit for trading the future. However, judiciously applied, knowing more aboutHowever, judiciously applied, knowing more about an instrumentan instrument’’s history prepares us better for thes history prepares us better for the future.future.
  18. 18. DataData--MiningMining –– InterpretingInterpreting Context/EnvironmentContext/Environment Examples of good dataExamples of good data--mining exercises:mining exercises: You want to trade stock XYZ. It would be aYou want to trade stock XYZ. It would be a good idea to find itsgood idea to find its ““pairspairs”” which are stockswhich are stocks which trade in a way which is either very similar,which trade in a way which is either very similar, or completely contrary, to the movements ofor completely contrary, to the movements of XYZ.XYZ. In the jargon, we need to look for uncorrelatedIn the jargon, we need to look for uncorrelated as well as correlated stocksas well as correlated stocks Here are examples:Here are examples:
  19. 19. Interpreting Context/EnvironmentInterpreting Context/Environment Uncorrelated Stocks 1Uncorrelated Stocks 1
  20. 20. DataData--MiningMining –– InterpretingInterpreting Context/EnvironmentContext/Environment Knowing what stocks are correlated orKnowing what stocks are correlated or uncorrelated with the ones you are primarilyuncorrelated with the ones you are primarily interested in provides you with a pertinentinterested in provides you with a pertinent universe of stocks to confirm technical signals,universe of stocks to confirm technical signals, and to implement risk management positionsand to implement risk management positions Buy/sell laggardsBuy/sell laggards Putting on Long/Short positions; hedgingPutting on Long/Short positions; hedging Early warning signals from charts of related stocksEarly warning signals from charts of related stocks
  21. 21. DataData--MiningMining –– InterpretingInterpreting Context/EnvironmentContext/Environment More on Environmental Issues:More on Environmental Issues: Do you know how the various sectors haveDo you know how the various sectors have performed? Knowing relative sectorperformed? Knowing relative sector performance is very important in trading stocks.performance is very important in trading stocks. Do you know how the stocks you are interestedDo you know how the stocks you are interested in have performed in their sector?in have performed in their sector?
  22. 22. Interpreting Context/EnvironmentInterpreting Context/Environment Sector performanceSector performance
  23. 23. DataData--MiningMining –– InterpretingInterpreting Context/EnvironmentContext/Environment More on Environmental Issues:More on Environmental Issues: If you wish to trade a stock and hold it forIf you wish to trade a stock and hold it for various time frames, do you know how it usuallyvarious time frames, do you know how it usually behaves?behaves? One can get to learn a stockOne can get to learn a stock’’s usual behavior bys usual behavior by being glued to its Level II screenbeing glued to its Level II screen –– how manyhow many stocks can one monitor like that?stocks can one monitor like that? Alternative : DataAlternative : Data--miningmining –– understand theunderstand the behavioral characteristics, in terms of the riskbehavioral characteristics, in terms of the risk and returns, of the stocks you tradeand returns, of the stocks you trade
  24. 24. Interpreting Context/EnvironmentInterpreting Context/Environment Individual stock riskIndividual stock risk--returnreturn
  25. 25. DataData--MiningMining –– InterpretingInterpreting Context/EnvironmentContext/Environment More on Environmental Issues:More on Environmental Issues: The example shows how MSFT has performed in theThe example shows how MSFT has performed in the last two years, if one were looking at six month timelast two years, if one were looking at six month time frames (assuming holding it for 6 months at a time).frames (assuming holding it for 6 months at a time). The dataThe data--mining shows that the stock has fallen by anmining shows that the stock has fallen by an amount that is extreme by historical standards, and theamount that is extreme by historical standards, and the risk is on the upside, not down.risk is on the upside, not down. An astute trader may look for technical signals to buyAn astute trader may look for technical signals to buy than to sell the stock at those levels.than to sell the stock at those levels.
  26. 26. DataData--MiningMining –– Interpreting RiskInterpreting Risk Interpreting Risk:Interpreting Risk: Talking about risk, would it not be prudent for an userTalking about risk, would it not be prudent for an user to be able to identify what risks are associated with anyto be able to identify what risks are associated with any stock he or she wants to trade?stock he or she wants to trade? This is done by constructing detailed historicalThis is done by constructing detailed historical distributions of returns, for every stock, so that thisdistributions of returns, for every stock, so that this provides a reality check of how risks are handled.provides a reality check of how risks are handled. Such dataSuch data--mining on the risk side is extremelymining on the risk side is extremely important as such information tells oneimportant as such information tells one what NOT to trade, even beforewhat NOT to trade, even before technical analysis is appliedtechnical analysis is applied
  27. 27. Interpreting RiskInterpreting Risk Individual stock riskIndividual stock risk
  28. 28. Interpreting RiskInterpreting Risk Risk of stock inside sectorRisk of stock inside sector
  29. 29. Interpreting RiskInterpreting Risk Risk reportsRisk reports
  30. 30. DataData--MiningMining –– Analytical SupportAnalytical Support Analytical Support:Analytical Support: Many things are possible.Many things are possible. Example: How to improve on ordinary Moving AverageExample: How to improve on ordinary Moving Average Crossover Analysis with dataCrossover Analysis with data--mining:mining: Moving Averages work best in trending markets or at leastMoving Averages work best in trending markets or at least markets which do not show muchmarkets which do not show much ““whipsawwhipsaw”” behaviorbehavior One cannot possibly take a position on every case of movingOne cannot possibly take a position on every case of moving average crossover that appears on oneaverage crossover that appears on one’’s charts or tradings charts or trading modelsmodels Some preliminary screening will go a long way in eliminatingSome preliminary screening will go a long way in eliminating false leadsfalse leads
  31. 31. DataData--MiningMining –– Analytical SupportAnalytical Support Consistency in PerformanceConsistency in Performance
  32. 32. DataData--MiningMining –– Analytical SupportAnalytical Support Another ExampleAnother Example For a stock (STAR) that has performed consistently overFor a stock (STAR) that has performed consistently over the last 12 months (daily), the application of MA analysisthe last 12 months (daily), the application of MA analysis should be fruitfulshould be fruitful Apply a 50 period MA to all time bars smaller than DailyApply a 50 period MA to all time bars smaller than Daily bars:bars: P/L for MA P/L for Buy and HoldP/L for MA P/L for Buy and Hold 5mb5mb --$51$51 --$201$201 10mb +$20910mb +$209 -- $282$282 15mb +$14615mb +$146 --$326$326 30mb30mb -- $59$59 --$295$295 60mb +$20460mb +$204 --$258$258 Daily +$853Daily +$853 +$687+$687
  33. 33. DataData--MiningMining –– Analytical SupportAnalytical Support Consistent PerformanceConsistent Performance
  34. 34. DataData--MiningMining –– Analytical SupportAnalytical Support Another Example:Another Example: For a stock (TRW) that has performed consistently over theFor a stock (TRW) that has performed consistently over the last six months (daily), the application of MA analysis lookslast six months (daily), the application of MA analysis looks like this:like this: Apply a 50 period MA to all time bars smaller than DailyApply a 50 period MA to all time bars smaller than Daily bars (300 bars before 22 Jul 02):bars (300 bars before 22 Jul 02): P/L for MA P/L for Buy and HoldP/L for MA P/L for Buy and Hold 5mb +$2635mb +$263 --$258$258 10mb +$9610mb +$96 -- $651$651 15mb +$50015mb +$500 --$620$620 30mb +$1830mb +$18 --$620$620 60mb +$51360mb +$513 --$494$494 Daily +$660Daily +$660 +$607+$607
  35. 35. DataData--MiningMining –– Analytical SupportAnalytical Support Summarizing:Summarizing: One tentative conclusion is that by picking stocks thatOne tentative conclusion is that by picking stocks that have shown consistent performance in the past (eitherhave shown consistent performance in the past (either up or down) and monitoring their performance on anup or down) and monitoring their performance on an ongoing basis, those that remain on a consistent path,ongoing basis, those that remain on a consistent path, can be screened for trading with very simple tools likecan be screened for trading with very simple tools like Moving Averages.Moving Averages. This is NOT meant to illustrate a specific tradingThis is NOT meant to illustrate a specific trading technique, but is only intended to show how datatechnique, but is only intended to show how data-- mining can add value to commonly used technicalmining can add value to commonly used technical analysis.analysis.
  36. 36. DataData--MiningMining –– Analytical SupportAnalytical Support AnotherAnother egeg –– Parametric OptimizationParametric Optimization
  37. 37. DataData--MiningMining –– Analytical SupportAnalytical Support This logic of using data mining to support technicalThis logic of using data mining to support technical analysis can be extended to theanalysis can be extended to the ““prepre--processingprocessing”” of rawof raw information, so that we can have betterinformation, so that we can have better assessments of whether stocks are in a situation to benefitassessments of whether stocks are in a situation to benefit from the application of traditional technical methods:from the application of traditional technical methods: Scanning for breakouts to trade channel breakout or volatilityScanning for breakouts to trade channel breakout or volatility breakout systems;breakout systems; Scanning for rebounds and dips to trade OscillatorsScanning for rebounds and dips to trade Oscillators Scanning for Volume spikesScanning for Volume spikes Scanning forScanning for FibonachiFibonachi levels for support and resistancelevels for support and resistance Scanning for Fundamentals for buying oversold stocksScanning for Fundamentals for buying oversold stocks Scanning for MAE/MFEScanning for MAE/MFE
  38. 38. DataData--MiningMining –– VisualizationVisualization DataData--Mining is not just about number crunching. ItMining is not just about number crunching. It is also about betteris also about better visualizationvisualization of relationshipsof relationships Put hard numbers into pictorial form, colorfulPut hard numbers into pictorial form, colorful pictures and relate difficultpictures and relate difficult--toto--interpret numbersinterpret numbers into a coherent whole and patterns may emergeinto a coherent whole and patterns may emerge So, dataSo, data--mining is also about usingmining is also about using ““pictures topictures to paint a thousand numberspaint a thousand numbers””. Turn geek. Turn geek--speak andspeak and greekgreek into maps.into maps. This is in fact one of the main objectives and usesThis is in fact one of the main objectives and uses of dataof data--mining.mining.
  39. 39. DataData--MiningMining –– VisualizationVisualization InIn the next slide, we will display a way ofthe next slide, we will display a way of visualizing thevisualizing the NasdaqNasdaq marketmarket Green for up and red for down stocks, inGreen for up and red for down stocks, in different degrees (shades) of changedifferent degrees (shades) of change Stocks are shown as big, mid and small capsStocks are shown as big, mid and small caps Can be regrouped to portray differentCan be regrouped to portray different aspects of price and volume behavioraspects of price and volume behavior
  40. 40. VisualizationVisualization -- HeatMapsHeatMaps NasdaqNasdaq Market, May 13, 02Market, May 13, 02
  41. 41. VisualizationVisualization –– Heat MapsHeat Maps The previousThe previous HeatMapHeatMap shows theshows the NasdaqNasdaq marketmarket on May 13on May 13thth 2002, long before the WCOM fraud2002, long before the WCOM fraud was announced.was announced. On that day, you could see the entire marketOn that day, you could see the entire market rallying, yet that stock was the biggest bigrallying, yet that stock was the biggest big--capcap loserloser Seeing the information is such stark contrast, oneSeeing the information is such stark contrast, one could assess its relative position in the market andcould assess its relative position in the market and maybe some could have been prescient enough tomaybe some could have been prescient enough to stay out. Traditional TA might have told you thatstay out. Traditional TA might have told you that it was oversold.it was oversold.
  42. 42. VisualizationVisualization –– Heat MapsHeat Maps NasdaqNasdaq Market, Jul 19, 02Market, Jul 19, 02
  43. 43. VisualizationVisualization –– Heat MapsHeat Maps That was theThat was the HeatMapHeatMap of theof the NasdaqNasdaq on 19 Julon 19 Jul 02, the day when the DJIA went down by 39002, the day when the DJIA went down by 390 pts to 8019 (pts to 8019 (--4.6%), seventh largest point loss in4.6%), seventh largest point loss in history thenhistory then One big cap, AMGN, was up, and there were aOne big cap, AMGN, was up, and there were a lot of small caps gaining on the daylot of small caps gaining on the day By seeing information in this form, one couldBy seeing information in this form, one could keep out of trouble or even capitalize onkeep out of trouble or even capitalize on opportunities.opportunities.
  44. 44. DataData--MiningMining -- VisualizationVisualization Visualization can be done on anything toVisualization can be done on anything to translate a massive amount of data intotranslate a massive amount of data into convenient, easyconvenient, easy--toto--interpret analyticsinterpret analytics One classic area is the very important Time &One classic area is the very important Time & Sales, which generate price and volumeSales, which generate price and volume informationinformation We can plot the data into various kinds of PriceWe can plot the data into various kinds of Price –– Volume chartsVolume charts
  45. 45. VisualizationVisualization –– Price Volume ChartsPrice Volume Charts PV Chart, EPV Chart, E--bay, Jul 22, 02bay, Jul 22, 02
  46. 46. DataData--MiningMining –– Developing FocusDeveloping Focus Some examples:Some examples: If you wish to use a particular technicalIf you wish to use a particular technical indicator, do you know if it has been successfulindicator, do you know if it has been successful on a stock in the past, or what stocks were goodon a stock in the past, or what stocks were good stocks to trade using that indicator? Or for astocks to trade using that indicator? Or for a given stock, what indicators should be used?given stock, what indicators should be used? DataData--mining can shed light on these keymining can shed light on these key questions, again narrowing our focus to what isquestions, again narrowing our focus to what is most likely to be successfulmost likely to be successful
  47. 47. DataData--MiningMining –– Developing FocusDeveloping Focus Best Stock ScanBest Stock Scan
  48. 48. DataData--MiningMining –– Developing FocusDeveloping Focus Best Indicator ScanBest Indicator Scan
  49. 49. DataData--MiningMining –– Developing FocusDeveloping Focus In indicator analysis, with typical American abundance,In indicator analysis, with typical American abundance, there is a lot of choice. The universe is now more thanthere is a lot of choice. The universe is now more than a hundred indicators, most of which need to be visuallya hundred indicators, most of which need to be visually interpreted. There are all kinds of time bars to analyze.interpreted. There are all kinds of time bars to analyze. Is there too much choice?Is there too much choice? There are many indicators that have specific buyThere are many indicators that have specific buy--sellsell rules.rules. Can we apply dataCan we apply data--mining to find out what they ALL saymining to find out what they ALL say about a particular stock at a given time?about a particular stock at a given time? How about what time bar is best for a particular indicator onHow about what time bar is best for a particular indicator on a particular stock?a particular stock?
  50. 50. DataData--MiningMining –– Developing FocusDeveloping Focus Indicator summaryIndicator summary
  51. 51. DataData--MiningMining –– Developing FocusDeveloping Focus The slide shows howThe slide shows how datadata--mining can bemining can be applied innovatively toapplied innovatively to create neat summariescreate neat summaries Indicator summaryIndicator summary
  52. 52. DataData--MiningMining –– Developing FocusDeveloping Focus Example 8:Example 8: How about eliminatingHow about eliminating some confusion as tosome confusion as to which time bar to use?which time bar to use? Can we find out what hasCan we find out what has worked in the recent past?worked in the recent past? Time bar analysisTime bar analysis
  53. 53. DataData--MiningMining –– Putting it togetherPutting it together DataData--mining can be used on its own, butmining can be used on its own, but would reinforce the two other major pieces ofwould reinforce the two other major pieces of technical market analyses :technical market analyses : BacktestingBacktesting andand Risk ManagementRisk Management Comprehensive Approach to TechnicalComprehensive Approach to Technical Trading:Trading: Step 1: DataStep 1: Data--MiningMining Step 2: HistoricalStep 2: Historical BacktestingBacktesting Step 3: Risk ManagementStep 3: Risk Management
  54. 54. RealReal--time Backtime Back--testingtesting HistoricalHistorical BacktestingBacktesting
  55. 55. BackBack--testing: Portfolio Diversificationtesting: Portfolio Diversification Risk ManagementRisk Management -- tradingtrading diversified systems anddiversified systems and using portfoliosusing portfolios
  56. 56. It’s the whole DataData--Mining:Mining: GooglingGoogling the Streamingthe Streaming PricesPrices The Essence ofThe Essence of AlgoAlgo Trading with an Edge :Trading with an Edge : Trading with an Edge =Trading with an Edge = prepre--simulationsimulation DataData--Mining + StrategyMining + Strategy BacktestingBacktesting ++ postpost simulationsimulation Risk ManagementRisk Management == !!
  57. 57. Thank YouThank You Guan Seng Khoo, PhDGuan Seng Khoo, PhD Head, Global Risk (Models Validation)Head, Global Risk (Models Validation) Group Risk AnalyticsGroup Risk Analytics Standard Chartered BankStandard Chartered Bank +65 9825 2148;+65 9825 2148; gskhoo@gmail.comgskhoo@gmail.com ExEx--AlgoAlgo Trading Developer & Fund Mgr,Trading Developer & Fund Mgr, ManMan--DrapeauDrapeau Group, part of the ManGroup, part of the Man Group, 1996Group, 1996--20022002

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