Technical Analysis Introduction

Total Page:16

File Type:pdf, Size:1020Kb

Technical Analysis Introduction Technical Analysis Introduction Ben Acton-Bond & Jorgen Drageset March. 13, 2018 BSFS Committee Meeting Principles of Technical Analysis • Technical analysis is based on the assumption that historical behavioral Historical Behavior patterns tend to repeat given a basic psychological framework Patterns • Patterns are shown in trends and formations • Trends become present over time whilst formations shows changes in trends Trends and • Formations give rise to uncertainty over the possibility of the continuation of a Formations trend • Hedge funds use TA, though it is very difficult to establish whether it’s profitable for them or not Applications • Technical analysis alone does not predict price movements Definition (1/4) Reading diagrams through candlesticks • Candlesticks uses time series • To construct a candlestick chart you must have access to the highest, lowest, opening and closing prices Interpreting Candlesticks • Long positively/negatively colored real bodies represent that the instrument has strengthened/weakened through the period. This is a bull/bear signal • If a candlestick has no shadows it is known as a Marubozu. This shows very strong bull/bear behavior • If the opening and closing price are identical, the candlestick will be represented in a cross. This is also called a Doji. This is an indicator of uncertainty as the price has moved through the day, yet closes near opening price Definition (2/4) Volumes as Technical Indicators • Volume is used as a technical indicator in technical analysis • It indicates the magnitude of the turnover in a stock • Volume should follow price changes as an increase in price should be justified by an increase in interest for the instrument Technical Price Volume Strength Increase Increase Strong Increase Decrease Neutral Volume Decrease Increase Weak (1) Decrease Decrease Neutral (1) Chart from Trendtech, following the OBX index. Definition (3/4) Support and resistance in technical charts • Support and resistance are measures that quantify potential psychological reactions in markets • These levels are determined through upper and lower bounds of horizontal trend channels • A safety margin of 3% is commonly used to account for ‘random’ movements around support and Resistance resistance levels • Trend channels are ever changing, thus making in vital for technical traders to reevaluate markets frequently. • The more data used to justify resistance and support SupportVlume levels, the more accurate these levels should be. Note that safety margin should be greater/smaller in longer/shorter trends. (1) (1) Chart from Trendtech, following the OBX index. Definition (4/4) Finding trends • A trend is the direction the price of an instrument is moving at a given time. In TA, trends are usually drawn in trend channels consisting of an upper and a lower trend line. These are found through finding trends in recent peaks and troughs. This process should be self explanatory. (1) Chart from Trendtech, not following real data Formations (2/3) Finding patterns • Technical analysis is based on the fact that historical behavioral patterns in the financial markets tend to repeat 4 Major Patterns 1 2 3 4 Head & Double Top / Flag Pennant Shoulders Double Bottom • Continuation Pattern • Continuation Pattern • Reversal Pattern • Reversal Pattern • Forms a tight flag • Chart looks like a small • Psychological • Psychological shape after an triangle motivation: failure to Motivation: failure to established trend and • Psychological reach new highs / lows beat previous highs / breaks out motivation: profit shakes traders out lows • Psychological taking motivation: profit taking Formations (2/3) Finding patterns: continuations 1 2 Charts from Trendtech, not following real data. Formations (3/3) Finding patterns: reversals 3 4 Support Charts from Trendtech, not following real data. Other Technical Indicators For advanced TA using appropriate software • RSI compares the sum of a price increase to that of a decrease over a RSI period • RSI = 100 * RSIup/(RSIup + RSIdown) • A Stochastic analysis will compare the moving average of one instrument Stochastics to another instrument and the relative convergence/divergence of the two can indicate strength/weakness • MACD can be applied by comparing 12-day vs 26-days moving average MACD • If the 12 day average lays above the 26 day average it indicates positive momentum Moving Average • Moving Averages measure the average price of an instrument over time.
Recommended publications
  • Predicting SARS-Cov-2 Infection Trend Using Technical Analysis Indicators
    medRxiv preprint doi: https://doi.org/10.1101/2020.05.13.20100784; this version posted May 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Predicting SARS-CoV-2 infection trend using technical analysis indicators Marino Paroli and Maria Isabella Sirinian Department of Clinical, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Italy ABSTRACT COVID-19 pandemic is a global emergency caused by SARS-CoV-2 infection. Without efficacious drugs or vaccines, mass quarantine has been the main strategy adopted by governments to contain the virus spread. This has led to a significant reduction in the number of infected people and deaths and to a diminished pressure over the health care system. However, an economic depression is following due to the forced absence of worker from their job and to the closure of many productive activities. For these reasons, governments are lessening progressively the mass quarantine measures to avoid an economic catastrophe. Nevertheless, the reopening of firms and commercial activities might lead to a resurgence of infection. In the worst-case scenario, this might impose the return to strict lockdown measures. Epidemiological models are therefore necessary to forecast possible new infection outbreaks and to inform government to promptly adopt new containment measures. In this context, we tested here if technical analysis methods commonly used in the financial market might provide early signal of change in the direction of SARS-Cov-2 infection trend in Italy, a country which has been strongly hit by the pandemic.
    [Show full text]
  • A Test of Macd Trading Strategy
    A TEST OF MACD TRADING STRATEGY Bill Huang Master of Business Administration, University of Leicester, 2005 Yong Soo Kim Bachelor of Business Administration, Yonsei University, 200 1 PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF BUSINESS ADMINISTRATION In the Faculty of Business Administration Global Asset and Wealth Management MBA O Bill HuangIYong Soo Kim 2006 SIMON FRASER UNIVERSITY Fall 2006 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author. APPROVAL Name: Bill Huang 1 Yong Soo Kim Degree: Master of Business Administration Title of Project: A Test of MACD Trading Strategy Supervisory Committee: Dr. Peter Klein Senior Supervisor Professor, Faculty of Business Administration Dr. Daniel Smith Second Reader Assistant Professor, Faculty of Business Administration Date Approved: SIMON FRASER . UNI~ER~IW~Ibra ry DECLARATION OF PARTIAL COPYRIGHT LICENCE The author, whose copyright is declared on the title page of this work, has granted to Simon Fraser University the right to lend this thesis, project or extended essay to users of the Simon Fraser University Library, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational institution, on its own behalf or for one of its users. The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection (currently available to the public at the "lnstitutional Repository" link of the SFU Library website <www.lib.sfu.ca> at: ~http:llir.lib.sfu.calhandlell8921112~)and, without changing the content, to translate the thesislproject or extended essays, if .technically possible, to any medium or format for the purpose of preservation of the digital work.
    [Show full text]
  • Finance Feature
    finance feature by Douglas Carlsen, DDS Face it: dentists are competitive and compulsive. We have to be to perform the miracles of our daily work. When I tell the average person that tooth “preparation” is performed with a drill running at 400,000rpm on a moving target within 1/16 inch or less of the nerve 90 percent of the time, I often hear, “No wonder you guys scare me to death!” With that compulsive drive comes the idea that we can invest smarter than the average Joe. Yet, according to noted author Larry Swedroe: “…the purchase by investors of individ- ual stocks… would seem to be the ultimate in controlling your own portfolio. However, in pursuing this course, you create two problems. First, you likely cannot achieve the extensive diversification that the use of mutual funds accomplishes. Second, the evidence tells us that individual investors who select their own stocks underperform appropriate benchmarks by significant margins.”1 Financial planners who utilize academic-based strategy agree that individuals cause little damage by actively trading a small portion of the portfolio (five to 10 percent) as long as the great bulk of one’s investments are in passive index funds. Nevertheless, many doctors choose to actively trade a significant portion of their funds. Since many of you will or already have taken the trading plunge, let’s examine the basics, then hear comments from a dentist who has done well since 2001 with active trading. Active traders normally use fundamental analysis or technical analysis, and often both. 1. Larry Swedroe, Investment Mistakes Even Smart Investors Make, McGraw Hill, 2012, p.24.
    [Show full text]
  • Proquest Dissertations
    INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy sutxnitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indisünct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. Bell & Howell Information and Leaming 300 North Zeeb Road. Ann Arbor, Ml 48106-1346 USA 800-521-0600 UMÏ METAPHORS OF EXCHANGE AND THE SHANGHAI STOCK MARKET DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the Graduate School o f The Ohio State University By Susan Diane Menke, M A ***** The Ohio State University 2000 Dissertation committee: Approved by: Dr.
    [Show full text]
  • 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.
    [Show full text]
  • Technical Analysis: Technical Indicators
    Chapter 2.3 Technical Analysis: Technical Indicators 0 TECHNICAL ANALYSIS: TECHNICAL INDICATORS Charts always have a story to tell. However, from time to time those charts may be speaking a language you do not understand and you may need some help from an interpreter. Technical indicators are the interpreters of the Forex market. They look at price information and translate it into simple, easy-to-read signals that can help you determine when to buy and when to sell a currency pair. Technical indicators are based on mathematical equations that produce a value that is then plotted on your chart. For example, a moving average calculates the average price of a currency pair in the past and plots a point on your chart. As your currency chart moves forward, the moving average plots new points based on the updated price information it has. Ultimately, the moving average gives you a smooth indication of which direction the currency pair is moving. 1 2 Each technical indicator provides unique information. You will find you will naturally gravitate toward specific technical indicators based on your TRENDING INDICATORS trading personality, but it is important to become familiar with all of the Trending indicators, as their name suggests, identify and follow the trend technical indicators at your disposal. of a currency pair. Forex traders make most of their money when currency pairs are trending. It is therefore crucial for you to be able to determine You should also be aware of the one weakness associated with technical when a currency pair is trending and when it is consolidating.
    [Show full text]
  • A Linear Process Approach to Short-Term Trading Using the VIX Index As a Sentiment Indicator
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 July 2021 Article A Linear Process Approach to Short-term Trading Using the VIX Index as a Sentiment Indicator Yawo Mamoua Kobara 1,‡ , Cemre Pehlivanoglu 2,‡* and Okechukwu Joshua Okigbo 3,‡ 1 Western University; [email protected] 2 Cidel Financial Services; [email protected] 3 WorldQuant University; [email protected] * Correspondence: [email protected] ‡ These authors contributed equally to this work. 1 Abstract: One of the key challenges of stock trading is the stock prices follow a random walk 2 process, which is a special case of a stochastic process, and are highly sensitive to new information. 3 A random walk process is difficult to predict in the short-term. Many linear process models that 4 are being used to predict financial time series are structural models that provide an important 5 decision boundary, albeit not adequately considering the correlation or causal effect of market 6 sentiment on stock prices. This research seeks to increase the predictive capability of linear process 7 models using the SPDR S&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for 8 market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto 9 Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional 10 Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated 11 into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence 12 (MACD), focusing on forecast performance. The profitability of various algorithmic trading 13 strategies are compared based on a combination of these two indicators.
    [Show full text]
  • Trading in the Australian Stockmarket Using Artificial Neural Networks
    Bond University DOCTORAL THESIS Trading in the Australian Stockmarket Using Artificial Neural Networks Vanstone, Bruce J Award date: 2005 Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. School of Information Technology Bond University Trading in the Australian Stockmarket using Artificial Neural Networks by Bruce James Vanstone Submitted to Bond University in fulfillment of the requirements for the degree Doctor of Philosophy November 2005 Abstract This thesis focuses on training and testing neural networks for use within stockmarket trading systems. It creates and follows a well defined methodology for developing and benchmarking trading systems which contain neural networks. Four neural networks and consequently four trading systems are presented within this thesis. The neural networks are trained using all fundamental or all technical variables, and are trained on different segments of the Australian stockmarket, namely all ordinary shares, and the S&P/ASX200 constituents. Three of the four trading systems containing neural networks significantly outperform the respective buy-and-hold returns for their segments of the market, demonstrating that neural networks are suitable for inclusion in stockmarket trading systems.
    [Show full text]
  • Relative Strength Index (RSI)
    Understanding Technical Analysis : Relative Strength Index (RSI) Understanding Relative Strength Index Hi 74.57 Relative Strength Index (RSI) is a technical indicator that is categorised under Potential supply momentum indicator. Basically, all momentum indicators measures thedisruption rate dueof riseto and fall of the financial instrument's price. Usually, momentum attacksindicators on two oilare dependent indicators as they are best used with other indicators sincetankers they near do Iran not tell the traders or analysts the potential direction of the financial instrument. Among Brent the popular type of momentum indicators are Stochastic Indicator, Commodity Channel Index and Relative Strength Index. In this factsheet, we will explore the Relative Strength Index or better known as RSI. What is RSI? WTI Developed by J. Welles Wilder Jr. in his seminal 1978 book, "New Concepts in Technical Trading WTI Systems." Measures the speed and change of price movement of the financial instruments. As RSI is a type of oscillator, thisLo 2,237.40indicator is represented as a set of line that has(24 values Mar 2020) from 0 to 100. Generally, a reading below 30 indicatesLo 18,591.93 an oversold condition, while a value above (2470 Mar signals 2020) an overbought condition. Leading vs Lagging Indicator RSI is a leading type of indicator. A leading type of indicator is an indicator that can provide the traders or analyst with future price movement. Another example of leading indicator is Stochastic Indicator. In contrast, a lagging indicator is an indicator that follows the price movement of the financial instruments. Despite their lagging nature in providing trading signals, traders or analysts prefer to use lagging indicators as they are more reliable.
    [Show full text]
  • Steve Nison, CMT President: Candlecharts.Com
    With Steve Nison, CMT President: Candlecharts.com Legal Notice: This recording is © Candlecharts.com and may not be copied, retransmitted, nor distributed in any manner whatsoever, including, but not limited to, video or audio file sharing sites, online auction and classified sites, discussion forums nor any other means. Illegal redistribution of this content may result in criminal and/or civil fines, pursuant to applicable international copyright law. All rights reserved worldwide. Candlestick Candlesticks + Charting Western Techniques Indicators Candlesticks + Trade Management ANATOMY OF THE CANDLESTICK LINE high Shadow close open Real Body open close low Real Bodies / Shadows Foundation of East + West: Nison Candlessticks and Trend Lines Horizontal Trend Line Change Polarity Crack/Snap Falling Off the Roof Who’s in control? Who’s in control? Scenario 1 Scenario 2 www.candlecharts.com Candles and Trend lines www.candlecharts.com How Education Saves You Big $$$ Major resistance 2.00-2.01 www.candlecharts.com How Education Saves You Big $$$ Long term resistance zone www.candlecharts.com How Education Saves You Big $$$ Long term resistance zone www.candlecharts.com Reading the Market’s Message with the Light of the Candles www.candlecharts.com www.candlecharts.com Resistance Resistance Support Support www.candlecharts.com Trading Ultra Shorts Support or resistance lines using longer term charts (slide 1 of 2) Long Term Chart Long term support once broken becomes…. New resistance And adding them to a Shorter Term Chart (slide 2 of 2)
    [Show full text]
  • A Study on Technical Indicators in Stock Price Movement Prediction Using Decision Tree Algorithms
    American Journal of Engineering Research (AJER) 2016 American Journal of Engineering Research (AJER) e-ISSN: 2320-0847 p-ISSN : 2320-0936 Volume-5, Issue-12, pp-207-212 www.ajer.org Research Paper Open Access A Study on Technical Indicators in Stock Price Movement Prediction Using Decision Tree Algorithms J Sharmila Vaiz1, Dr M Ramaswami 2 1Ph.D. Research Scholar, Dept. of Comp. Applns. Madurai Kamaraj University, Madurai, India 2Associate Professor, Dept. of Comp. Applns, Madurai Kamaraj University, Madurai, India ABSTRACT: Predicting stock price movement is a highly challenging task as the nature of the stock prices are quite noisy, dynamic, complicated, non-parametric, chaotic and non-linear. The toughest job in stock market is to examine the financial time series data and make decisions which improve the investment returns and to minimize the loss incurred. Technical analysis is a trading tool that evaluates securities and attempts to forecast their future movement by analyzing price and volume data. Many traditional statistical tools are available for the investors for making decision in financial market. Many technical indicators such as Moving Averages (SMA, EMA, WMA, VWMA, DEMA), Trend Indictors (MACD, ADX, TDI, Aroon, VHF), Momentum indicators (Stochastic, RSI, SMI, WPR, CMO,CCI), Volatility indicators (BBands, ATR, Dochain Channel) and Volume indicators(OBV, MFI, CMF) are available to analyze the stock price movement. In this study, decision tree classification method is used to analyze the role of these technical indicators in predicting stock price movement of six high market capitalization companies of NSE. Keywords: Technical indicators, Decision tree analysis, ROC curve, AUC I.
    [Show full text]
  • Research Article Predicting Stock Price Trend Using MACD Optimized by Historical Volatility
    Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 9280590, 12 pages https://doi.org/10.1155/2018/9280590 Research Article Predicting Stock Price Trend Using MACD Optimized by Historical Volatility Jian Wang and Junseok Kim Department of Mathematics, Korea University, Seoul , Republic of Korea Correspondence should be addressed to Junseok Kim; [email protected] Received 18 September 2018; Revised 13 November 2018; Accepted 21 November 2018; Published 25 December 2018 Academic Editor: Luis Mart´ınez Copyright © 2018 Jian Wang and Junseok Kim. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the rapid development of the fnancial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more signifcantly to recent price changes than the simple moving average (SMA). Traders fnd the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. Te purpose of this study is to develop an efective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fxed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD.
    [Show full text]