Technical Analysis for Algorithmic Pattern Recognition PDF
Preview Technical Analysis for Algorithmic Pattern Recognition
Prodromos E. Tsinaslanidis Achilleas D. Zapranis Technical Analysis for Algorithmic Pattern Recognition Technical Analysis for Algorithmic Pattern Recognition ThiSisaFMBlankPage Prodromos E. Tsinaslanidis (cid:129) Achilleas D. Zapranis Technical Analysis for Algorithmic Pattern Recognition ProdromosE.Tsinaslanidis AchilleasD.Zapranis TheBusinessSchool DepartmentofAccountingandFinance CanterburyChristChurchUniversity UniversityofMacedonia Canterbury,Kent Thessaloniki,Greece UnitedKingdom ISBN978-3-319-23635-3 ISBN978-3-319-23636-0 (eBook) DOI10.1007/978-3-319-23636-0 LibraryofCongressControlNumber:2015955395 SpringerChamHeidelbergNewYorkDordrechtLondon ©SpringerInternationalPublishingSwitzerland2016 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexempt fromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthis book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade. Noinformationprovidedinthisbookshouldbeconstruedasinvestmentortradingadviceoranofferto sellinvestmentadviceoranyinvestmentproduct.Norepresentationisbeingmadethatanytechnical tradingstrategydescribedwithinthisbookwillorislikelytoachieveprofitsorlossessimilartothose shown.Pastperformanceisnotindicativeoffutureperformance.Itshouldbenotedthatmarketscango upordownand,toourknowledge,thereisnoperfecttechniqueforinvestingandtrading.Sotheauthors cannotbedeemedresponsibleforanylossesarisingfromtheinformationandtoolsprovidedhere. Printedonacid-freepaper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com) To our families ThiSisaFMBlankPage Preface Technical analysisisamethodological framework ofanalyzing,primarilygraphi- cally, the historical evolution of financial assets’ prices and inferring from this assessment future predictions. Technicians use a variety of technical tools within their trading activities, like filter rules, technical indicators, patterns, and candle- sticks.Althoughmostacademicsregardtechnicalanalysiswithgreatskepticism,a significant proportion of practitioners consider technical recommendation within theirtradingactivities.Technical analysisisbeingusedeitherbyacademicsasan “economictest”oftheweak-formefficientmarkethypothesisorbypractitionersas amainorsupplementarytoolforderivingtradingsignals. This book focuses mainly on technical patterns, a topic where existed bibliog- raphy usually suffers from critical problems. Books on technical analysis mainly deal with technical indicators, and when referring to patterns, the approach followed is most of times theoretical and descriptive rather than scientific and quantitative. In some cases, only optimal examples are illustrated, which might givethefalseimpressiontoreaders,lackingtherequiredscientificbackground,that charting is most of the times profitable. Statistical framework for assessing the realized returns isalsousually absent.Subjectivity embedded intheidentification oftechnicalpatternsviavisualassessmentandvariouscognitivebiasesthataffect the trading and investment activities of many practitioners place barriers in an unbiasedassessmentoftechnicalpatterns. The purpose of this book is to deal with the aforementioned problems by approaching technical analysis in a systematic way. This is achieved through developingnovelrule-basedpatternrecognizers andimplementingstatisticaltests for assessing their performance. Our proposed methodology is based on the algo- rithmicandthusunbiasedpatternrecognition.Thephilosophybehindthedesignof theproposedalgorithmsistocapturethetheoreticalprinciplesfoundinthelitera- ture for recognizing visually technical patterns and to quantify them accordingly. Themethodologicalframeworkwepresentmayprovetobeusefulforbothfuture vii viii Preface academic studies that test the null hypothesis of the weak-form market efficiency and practitioners who want to embed technical patterns within their trading decision-makingprocesses. Canterbury,UnitedKingdom ProdromosE.Tsinaslanidis Thessaloniki,Greece AchilleasD.Zapranis List of Abbreviations APT ArbitragePricingTheory BB BollingerBands CAPM CapitalAssetPricingModel DB DoubleBottoms DDTW DerivativeDynamicTimeWarping DT DoubleTops DTW DynamicTimeWarping EMA ExponentialMovingAverage EMH EfficientMarketHypothesis GARCH GeneralizedAutoregressiveConditionalHeteroskedasticity GBM GeometricBrownianMotion HH HighestHigh HS HeadandShoulders HSAR HorizontalSupportandResistanceLevel HSARz HorizontalSupportandResistanceZone IID IndependentandIdenticallyDistributed INID IndependentandNotIdenticallyDistributed IPOCID IndependentPredictionofChangeinDirection LL LowestLow LWMA LinearlyWeightedMovingAverage MA MovingAverage MAC MovingAverageCrossovers MACD MovingAverageConvergenceDivergence MAE MeanAbsoluteError MAPE MeanAbsolutePercentageError MOM Momentum MSE MeanSquaredError NPRMSE Normalized(by)PersistenceRootMeanSquaredError NRMSE NormalizedRootMeanSquaredError PIPs PerceptuallyImportantPoints POCID PredictionofChangeinDirection ix
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