Technical Analysis for Algorithmic Pattern Recognition

213 Pages·2016·3.2 MB·English

by  Prodromos E. Tsinaslanidis

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
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tradingstrategydescribedwithinthisbookwillorislikelytoachieveprofitsorlossessimilartothose
shown.Pastperformanceisnotindicativeoffutureperformance.Itshouldbenotedthatmarketscango
upordownand,toourknowledge,thereisnoperfecttechniqueforinvestingandtrading.Sotheauthors
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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
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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
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