Technical Analysis
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A Statistical Analysis of the Predictive Power of Japanese Candlesticks Mohamed Jamaloodeen Georgia Gwinnett College, [email protected]
Journal of International & Interdisciplinary Business Research Volume 5 Article 5 June 2018 A Statistical Analysis of the Predictive Power of Japanese Candlesticks Mohamed Jamaloodeen Georgia Gwinnett College, [email protected] Adrian Heinz Georgia Gwinnett College, [email protected] Lissa Pollacia Georgia Gwinnett College, [email protected] Follow this and additional works at: https://scholars.fhsu.edu/jiibr Part of the Finance and Financial Management Commons Recommended Citation Jamaloodeen, Mohamed; Heinz, Adrian; and Pollacia, Lissa (2018) "A Statistical Analysis of the Predictive Power of Japanese Candlesticks," Journal of International & Interdisciplinary Business Research: Vol. 5 , Article 5. Available at: https://scholars.fhsu.edu/jiibr/vol5/iss1/5 This Article is brought to you for free and open access by FHSU Scholars Repository. It has been accepted for inclusion in Journal of International & Interdisciplinary Business Research by an authorized editor of FHSU Scholars Repository. Jamaloodeen et al.: Analysis of Predictive Power of Japanese Candlesticks A STATISTICAL ANALYSIS OF THE PREDICTIVE POWER OF JAPANESE CANDLESTICKS Mohamed Jamaloodeen, Georgia Gwinnett College Adrian Heinz, Georgia Gwinnett College Lissa Pollacia, Georgia Gwinnett College Japanese Candlesticks is a technique for plotting past price action of a specific underlying such as a stock, index or commodity using open, high, low and close prices. These candlesticks create patterns believed to forecast future price movement. Although the candles’ popularity has increased rapidly over the last decade, there is still little statistical evidence about their effectiveness over a large number of occurrences. In this work, we analyze the predictive power of the Shooting Star and Hammer patterns using over six decades of historical data of the S&P 500 index. -
Copyrighted Material
INDEX Page numbers followed by n indicate note numbers. A Arnott, Robert, 391 Ascending triangle pattern, 140–141 AB model. See Abreu-Brunnermeier model Asks (offers), 8 Abreu-Brunnermeier (AB) model, 481–482 Aspray, Thomas, 223 Absolute breadth index, 327–328 ATM. See Automated teller machine Absolute difference, 327–328 ATR. See Average trading range; Average Accelerated trend lines, 65–66 true range Acceleration factor, 88, 89 At-the-money, 418 Accumulation, 213 Average range, 79–80 ACD method, 186 Average trading range (ATR), 113 Achelis, Steven B., 214n1 Average true range (ATR), 79–80 Active month, 401 Ayers-Hughes double negative divergence AD. See Chaikin Accumulation Distribution analysis, 11 Adaptive markets hypothesis, 12, 503 Ayres, Leonard P. (Colonel), 319–320 implications of, 504 ADRs. See American depository receipts ADSs. See American depository shares B 631 Advance, 316 Bachelier, Louis, 493 Advance-decline methods Bailout, 159 advance-decline line moving average, 322 Baltic Dry Index (BDI), 386 one-day change in, 322 Bands, 118–121 ratio, 328–329 trading strategies and, 120–121, 216, 559 that no longer are profitable, 322 Bandwidth indicator, 121 to its 32-week simple moving average, Bar chart patterns, 125–157. See also Patterns 322–324 behavioral finance and pattern recognition, ADX. See Directional Movement Index 129–130 Alexander, Sidney, 494 classic, 134–149, 156 Alexander’s filter technique, 494–495 computers and pattern recognition, 130–131 American Association of Individual Investors learning objective statements, 125 (AAII), 520–525 long-term, 155–156 American depository receipts (ADRs), 317 market structure and pattern American FinanceCOPYRIGHTED Association, 479, 493 MATERIALrecognition, 131 Amplitude, 348 overview, 125–126 Analysis pattern description, 126–128 description of, 300 profitability of, 133–134 fundamental, 473 Bar charts, 38–39 Andrews, Dr. -
Stock Chart Pattern Recognition with Deep Learning
Stock Chart Pattern recognition with Deep Learning Marc Velay and Fabrice Daniel Artificial Intelligence Department of Lusis, Paris, France [email protected] http://www.lusis.fr June 2018 ABSTRACT the number of patterns recognized implies having a human examining candlestick charts in order to deduce signal char- This study evaluates the performances of CNN and LSTM acteristics before implementing the detection using condi- for recognizing common charts patterns in a stock historical tions specific to that pattern. Adding different parameters data. It presents two common patterns, the method used to to modulate ratios allows us to tweak the patterns’ charac- build the training set, the neural networks architectures and teristics. This technique does not have any generalization the accuracies obtained. potential. If the pattern is slightly outside of the defined Keywords: Deep Learning, CNN, LSTM, Pattern recogni- bounds, it will not be detected, even if a human would have tion, Technical Analysis classified it otherwise. Another solution is DTW3 which consists in computing 1 INTRODUCTION the distance between two time series. DTW allows us to Patterns are recurring sequences found in OHLC1 candle- recognize a pattern that could vary in size and length. To stick charts which traders have historically used as buy and use this algorithm, we must use reference time series, which sell signals. Several studies, notably by Bulkowski2, have have to be selected by a human. The references must gener- found some correlation between patterns and future trends, alize well when compared with signals similar to the pattern although to a limited extent. The correlations were found to in order to capture the whole range. -
Testing the Profitability of Technical Analysis in Singapore And
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ScholarBank@NUS Testing the Profitability of Technical Analysis in Singapore and Malaysian Stock Markets Department of Electrical and Computer Engineering Zoheb Jamal HT080461R In partial fulfillment of the requirements for the Degree of Master of Engineering National University of Singapore 2010 1 Abstract Technical Analysis is a graphical method of looking at the history of price of a stock to deduce the probable future trend in its return. Being primarily visual, this technique of analysis is difficult to quantify as there are numerous definitions mentioned in the literature. Choosing one over the other might lead to data- snooping bias. This thesis attempts to create a universe of technical rules, which are then tested on historical data of Straits Times Index and Kuala Lumpur Composite Index. The technical indicators tested are Filter Rules, Moving Averages, Channel Breakouts, Support and Resistance and Momentum Strategies in Price. The technical chart patterns tested are Head and Shoulders, Inverse Head and Shoulders, Broadening Tops and Bottoms, Triangle Tops and Bottoms, Rectangle Tops and Bottoms, Double Tops and Bottoms. This thesis also outlines a pattern recognition algorithm based on local polynomial regression to identify technical chart patterns that is an improvement over the kernel regression approach developed by Lo, Mamaysky and Wang [4]. 2 Acknowledgements I would like to thank my supervisor Dr Shuzhi Sam Ge whose invaluable advice and support made this research possible. His mentoring and encouragement motivated me to attempt a project in Financial Engineering, even though I did not have a background in Finance. -
Calibration of Bollinger Bands Parameters for Trading Strategy Development in the Baltic Stock Market
ISSN 1392 – 2785 Inzinerine Ekonomika-Engineering Economics, 2010, 21(3), 244-254 Calibration of Bollinger Bands Parameters for Trading Strategy Development in the Baltic Stock Market Audrius Kabasinskas, Ugnius Macys Kaunas University of Technology K. Donelaicio st. 73, LT-44029, Kaunas, Lithuania e-mail: [email protected], [email protected] In recent decades there was a robust boom in "Bollinger plotter" was developed using the most investment sector in Lithuania, as more people chose to popular mathematical toolbox MatLab in order to solve invest money in investment funds rather than keep money in stated problems. Application is capable of charting the closet. The Baltic States Market turnover has increased Bollinger Bands and 6 other technical indicators with from 721 MEUR in 2000 to 978 MEUR in 2008 (with peak desired period of time. This software is not a fully 2603 MEUR in 2005). When difficult period appeared in automated decision making system, as decisions are global markets, a lot of attention was dedicated towards the usually made based on value judgment. managing of investments. Investment management firms in Since the stock returns usually have distributions with Lithuania gain significance in personal as well as in fat tails, then less than 95% of data fit in the Bollinger business section increasingly; even though these firms are trading channels. However the Bollinger bands trading considerably young (the first one in Lithuania was signals were supported by additional indicators (e.g. %b), established in year 2000). so the loss of data is not significant. Successful investment begins with the financial Our calibration results show that short term investor analysis of stock, asset or index, which you are going to should apply 10 days moving average and use a trading invest. -
FOREX WAVE THEORY.Pdf
FOREX WAVE THEORY This page intentionally left blank FOREX WAVE THEORY A Technical Analysis for Spot and Futures Currency Traders JAMES L. BICKFORD McGraw-Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2007 by The McGraw-Hill Companies. All rights reserved. Manufactured in the United States of America. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. 0-07-151046-X The material in this eBook also appears in the print version of this title: 0-07-149302-6. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales pro- motions, or for use in corporate training programs. For more information, please contact George Hoare, Special Sales, at [email protected] or (212) 904-4069. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licen- sors reserve all rights in and to the work. Use of this work is subject to these terms. -
Forecasting Direction of Exchange Rate Fluctuations with Two Dimensional Patterns and Currency Strength
FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY MUSTAFA ONUR ÖZORHAN IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PILOSOPHY IN COMPUTER ENGINEERING MAY 2017 Approval of the thesis: FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH submitted by MUSTAFA ONUR ÖZORHAN in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Engineering Department, Middle East Technical University by, Prof. Dr. Gülbin Dural Ünver _______________ Dean, Graduate School of Natural and Applied Sciences Prof. Dr. Adnan Yazıcı _______________ Head of Department, Computer Engineering Prof. Dr. İsmail Hakkı Toroslu _______________ Supervisor, Computer Engineering Department, METU Examining Committee Members: Prof. Dr. Tolga Can _______________ Computer Engineering Department, METU Prof. Dr. İsmail Hakkı Toroslu _______________ Computer Engineering Department, METU Assoc. Prof. Dr. Cem İyigün _______________ Industrial Engineering Department, METU Assoc. Prof. Dr. Tansel Özyer _______________ Computer Engineering Department, TOBB University of Economics and Technology Assist. Prof. Dr. Murat Özbayoğlu _______________ Computer Engineering Department, TOBB University of Economics and Technology Date: ___24.05.2017___ I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work. Name, Last name: MUSTAFA ONUR ÖZORHAN Signature: iv ABSTRACT FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH Özorhan, Mustafa Onur Ph.D., Department of Computer Engineering Supervisor: Prof. -
Stairstops Using Magee’S Basing Points to Ratchet Stops in Trends
StairStops Using Magee’s Basing Points to Ratchet Stops in Trends This may be the most important book on stops of this decade for the general investor. Professor Henry Pruden, PhD. Golden Gate University W.H.C. Bassetti Coauthor/Editor Edwards & Magee’s Technical Analysis of Stock Trends, 9th Edition This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable date and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of MaoMao Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from MaoMao Press LLC for such copying. Direct all inquiries to MaoMao Press LLC, POB 88, San Geronimo, CA 94963-0088 Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. Dow–JonesSM, The DowSM, Dow–Jones Industrial AverageSM, and DJIASM are service marks of Dow– Jones & Company, Inc., and have been licensed for use for certain purposes by the Board of Trade of the City of Chicago (CBOT®). -
Charles Dow Desarrolla Lo Que Hoy Se Conoce Como El Dow Jones Industrial Average®
Hitos 1896 • Charles Dow desarrolla lo que hoy se conoce como el Dow Jones Industrial Average®. 1923 • Standard Statistics Company, antecesora de Standard & Poor’s, desarrolla su primer indicador del mercado accionario con cobertura a 233 empresas. 1926 • Standard Statistics Company lanza al mercado un índice compuesto de precios que incluía 90 acciones. 1941 • Standard Statistics se fusiona con Poor’s Publishing para formar Standard & Poor's. • El indicador del mercado accionario creado en 1923 aumenta su número de compañías de 233 a 416. 1946 • El Dow Jones Industrial Average cumple 50 años. 1957 • Standard & Poor’s publica por primera vez el S&P 500® como un índice de 500 acciones. 1972 • El S&P 500 se convierte en el primer índice bursátil con publicación diaria. 1975 • ExxonMobil se convierte en el primer fondo de pensiones vinculado al S&P 500 y la industria comienza a expandirse. • La calidad institucional llega al mercado general. Vanguard lanza el primer fondo mutuo de índices de consumo, el Vanguard 500, y utiliza el S&P 500 como benchmark. 1982 • CME Group comienza a operar los primeros índices de futuros ―S&P 500 index futures― en la Bolsa de Valores de Chicago (Chicago Mercantile Exchange). 1983 • El Mercado de Opciones de la Bolsa de Chicago (CBOE®) comienza a operar el primer índice de opciones. Estas opciones se basaban en el S&P 500 y el S&P 100. S&P Dow Jones Indices – Hitos 2016 1991 • Standard & Poor’s lanza al mercado el S&P MidCap 400®, el primer índice destacado de títulos de capitalización media en Estados Unidos. -
Results of the S&P Paris-Aligned & Climate Transition (PACT) Indices
INDEX ANNOUNCEMENT Results of the S&P Paris-Aligned & Climate Transition (PACT) Indices Consultation on Eligibility Requirements and Constraints AMSTERDAM, MAY 18, 2021: S&P Dow Jones Indices (“S&P DJI”) has conducted a consultation with market participants on potential changes to the S&P Paris-Aligned & Climate Transition (PACTTM) Indices. S&P DJI will make changes to the eligibility requirements and optimization constraints used in these indices. The changes are designed to ensure the indices continue to meet their objective, reflect evolving expectations for environmental, social and governance (ESG) business exclusions, while including additional stability to the turnover and stock counts following fluctuations caused by the existing rules. The table below summarizes the changes, and which indices they will impact. Index Family1 Methodology Change CT PA Current Updated Environmental X X CT: Weighted-average S&P DJI CT: Weighted-average S&P DJI ESG Score Score Environmental Score (waE) of the CT Index (waESG) of the CT Index should be ≥ the Constraint to should be ≥ the waE of the eligible universe. eligible waESG of the eligible universe. ESG Score Constraint PA: Weighted-average S&P DJI PA: Weighted-average S&P DJI ESG Score Environmental Score (waE) of the PA Index (waESG) of the PA Index should be ≥ the should be ≥ the waE of the eligible universe waESG of the universe after 20% of the + (20% × (max E score in eligible universe – worst ESG score performing companies by eligible universe’s waE)). count are removed and weight redistributed. Introduce X X No buffer, minimum stock weight lower Minimum stock weight threshold ≥1 buffer rule and threshold of 0.01%, maximum weight of 5%. -
Candlestick—The Main Mistake of Economy Research in High Frequency Markets
International Journal of Financial Studies Article Candlestick—The Main Mistake of Economy Research in High Frequency Markets Michał Dominik Stasiak Department of Investment and Real Estate, Poznan University of Economics and Business, al. Niepodleglosci 10, 61-875 Poznan, Poland; [email protected] Received: 4 August 2020; Accepted: 1 October 2020; Published: 10 October 2020 Abstract: One of the key problems of researching the high-frequency financial markets is the proper data format. Application of the candlestick representation (or its derivatives such as daily prices, etc.), which is vastly used in economic research, can lead to faulty research results. Yet, this fact is consistently ignored in most economic studies. The following article gives examples of possible consequences of using candlestick representation in modelling and statistical analysis of the financial markets. Emphasis should be placed on the problem of research results being detached from the investing practice, which makes most of the results inapplicable from the investor’s point of view. The article also presents the concept of a binary-temporal representation, which is an alternative to the candlestick representation. Using binary-temporal representation allows for more precise and credible research and for the results to be applied in investment practice. Keywords: high frequency econometric; technical analysis; investment decision support; candlestick representation; binary-temporal representation JEL Classification: C01; C53; C90 1. Introduction While researching any subject literature, often one can notice that some popular methods in scientific research are copied and used without second thought by further researchers. Nowadays, the vast majority of papers pertaining to the analysis of course trajectory on financial markets and connected prediction possibilities use historical data in the form of a candlestick representation (or its derivatives such as daily opening prices, usually called daily prices, etc.) (Burgess 2010; Kirkpatrick and Dahlquist 2010; Schlossberg 2012). -
Japanese Candlestick Patterns
Presents Japanese Candlestick Patterns www.ForexMasterMethod.com www.ForexMasterMethod.com RISK DISCLOSURE STATEMENT / DISCLAIMER AGREEMENT Trading any financial market involves risk. This course and all and any of its contents are neither a solicitation nor an offer to Buy/Sell any financial market. The contents of this course are for general information and educational purposes only (contents shall also mean the website http://www.forexmastermethod.com or any website the content is hosted on, and any email correspondence or newsletters or postings related to such website). Every effort has been made to accurately represent this product and its potential. There is no guarantee that you will earn any money using the techniques, ideas and software in these materials. Examples in these materials are not to be interpreted as a promise or guarantee of earnings. Earning potential is entirely dependent on the person using our product, ideas and techniques. We do not purport this to be a “get rich scheme.” Although every attempt has been made to assure accuracy, we do not give any express or implied warranty as to its accuracy. We do not accept any liability for error or omission. Examples are provided for illustrative purposes only and should not be construed as investment advice or strategy. No representation is being made that any account or trader will or is likely to achieve profits or losses similar to those discussed in this report. Past performance is not indicative of future results. By purchasing the content, subscribing to our mailing list or using the website or contents of the website or materials provided herewith, you will be deemed to have accepted these terms and conditions in full as appear also on our site, as do our full earnings disclaimer and privacy policy and CFTC disclaimer and rule 4.41 to be read herewith.