Technical analysis

In finance, technical analysis is an analysis methodology for forecasting the direction of prices through the study of past market data, primarily price and volume.[1] Behavioral economics and quantitative analysis use many of the same tools of technical analysis,[2][3][4] which, being an aspect of active management, stands in contradiction to much of modern portfolio theory. The efficacy of both technical and fundamental analysis is disputed by the efficient-market hypothesis which states that stock market prices are essentially unpredictable.[5]


The principles of technical analysis are derived from hundreds of years of financial market data.[6] Some aspects of technical analysis began to appear in Amsterdam-based merchant Joseph de la Vega's accounts of the Dutch financial markets in the 17th century. In Asia, technical analysis is said to be a method developed by Homma Munehisa during the early 18th century which evolved into the use of candlestick techniques, and is today a technical analysis charting tool.[7][8] In the 1920s and 1930s, Richard W. Schabacker published several books which continued the work of Charles Dow and William Peter Hamilton in their books Stock Market Theory and Practice and Technical Market Analysis. In 1948, Robert D. Edwards and John Magee published Technical Analysis of Stock Trends which is widely considered to be one of the seminal works of the discipline. It is exclusively concerned with trend analysis and chart patterns and remains in use to the present. Early technical analysis was almost exclusively the analysis of charts because the processing power of computers was not available for the modern degree of statistical analysis. Charles Dow reportedly originated a form of point and figure chart analysis.

Dow theory is based on the collected writings of Dow Jones co-founder and editor Charles Dow, and inspired the use and development of modern technical analysis at the end of the 19th century. Other pioneers of analysis techniques include Ralph Nelson Elliott, William Delbert Gann and Richard Wyckoff who developed their respective techniques in the early 20th century. More technical tools and theories have been developed and enhanced in recent decades, with an increasing emphasis on computer-assisted techniques using specially designed computer software.

General description

Fundamental analysts examine earnings, dividends, assets, quality, ratio, new products, research and the like. Technicians employ many methods, tools and techniques as well, one of which is the use of charts. Using charts, technical analysts seek to identify price patterns and market trends in financial markets and attempt to exploit those patterns.[9]

Technicians using charts search for archetypal price chart patterns, such as the well-known head and shoulders [10] or double top/bottom reversal patterns, study technical indicators, moving averages, and look for forms such as lines of support, resistance, channels, and more obscure formations such as flags, pennants, balance days and cup and handle patterns.[11]

Technical analysts also widely use market indicators of many sorts, some of which are mathematical transformations of price, often including up and down volume, advance/decline data and other inputs. These indicators are used to help assess whether an asset is trending, and if it is, the probability of its direction and of continuation. Technicians also look for relationships between price/volume indices and market indicators. Examples include the moving average, relative strength index, and MACD. Other avenues of study include correlations between changes in Options (implied volatility) and put/call ratios with price. Also important are sentiment indicators such as Put/Call ratios, bull/bear ratios, short interest, Implied Volatility, etc.

There are many techniques in technical analysis. Adherents of different techniques (for example: Candlestick analysis, the oldest form of technical analysis developed by a Japanese grain trader; Harmonics; Dow theory; and Elliott wave theory) may ignore the other approaches, yet many traders combine elements from more than one technique. Some technical analysts use subjective judgment to decide which pattern(s) a particular instrument reflects at a given time and what the interpretation of that pattern should be. Others employ a strictly mechanical or systematic approach to pattern identification and interpretation.

Contrasting with technical analysis is fundamental analysis, the study of economic factors that influence the way investors price financial markets. Technical analysis holds that prices already reflect all the underlying fundamental factors. Uncovering the trends is what technical indicators are designed to do, although neither technical nor fundamental indicators are perfect. Some traders use technical or fundamental analysis exclusively, while others use both types to make trading decisions.[12]


Technical analysis employs models and trading rules based on price and volume transformations, such as the relative strength index, moving averages, regressions, inter-market and intra-market price correlations, business cycles, stock market cycles or, classically, through recognition of chart patterns.

Technical analysis stands in contrast to the fundamental analysis approach to security and stock analysis. In the fundamental equation M = P/E technical analysis is the examination of M (multiple). Multiple encompasses the psychology generally abounding, i.e. the extent of willingness to buy/sell. Also in M is the ability to pay as, for instance, a spent-out bull can't make the market go higher and a well-heeled bear won't. Technical analysis analyzes price, volume, psychology, money flow and other market information, whereas fundamental analysis looks at the facts of the company, market, currency or commodity. Most large brokerage, trading group, or financial institutions will typically have both a technical analysis and fundamental analysis team.

In the 1960s and 1970s it was widely dismissed by academics. In a recent review, Irwin and Park[13] reported that 56 of 95 modern studies found that it produces positive results but noted that many of the positive results were rendered dubious by issues such as data snooping, so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience.[14] Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient-market hypothesis.[15][16] Users hold that even if technical analysis cannot predict the future, it helps to identify trends, tendencies, and trading opportunities.[17]

While some isolated studies have indicated that technical trading rules might lead to consistent returns in the period prior to 1987,[18][19][20][21] most academic work has focused on the nature of the anomalous position of the foreign exchange market.[22] It is speculated that this anomaly is due to central bank intervention, which obviously technical analysis is not designed to predict.[23] Recent research suggests that combining various trading signals into a Combined Signal Approach may be able to increase profitability and reduce dependence on any single rule.[24]


Soporte-resistencia reverseroles
Stock chart showing levels of support (4,5,6, 7, and 8) and resistance (1, 2, and 3); levels of resistance tend to become levels of support and vice versa.

A core principle of technical analysis is that a market's price reflects all relevant information impacting that market. A technical analyst therefore looks at the history of a security or commodity's trading pattern rather than external drivers such as economic, fundamental and news events. It is believed that price action tends to repeat itself due to the collective, patterned behavior of investors. Hence technical analysis focuses on identifiable price trends and conditions.[25][26]

Market action discounts everything

Based on the premise that all relevant information is already reflected by prices, technical analysts believe it is important to understand what investors think of that information, known and perceived.

Prices move in trends

Technical analysts believe that prices trend directionally, i.e., up, down, or sideways (flat) or some combination. The basic definition of a price trend was originally put forward by Dow theory.[9]

An example of a security that had an apparent trend is AOL from November 2001 through August 2002. A technical analyst or trend follower recognizing this trend would look for opportunities to sell this security. AOL consistently moves downward in price. Each time the stock rose, sellers would enter the market and sell the stock; hence the "zig-zag" movement in the price. The series of "lower highs" and "lower lows" is a tell tale sign of a stock in a down trend.[27] In other words, each time the stock moved lower, it fell below its previous relative low price. Each time the stock moved higher, it could not reach the level of its previous relative high price.

Note that the sequence of lower lows and lower highs did not begin until August. Then AOL makes a low price that does not pierce the relative low set earlier in the month. Later in the same month, the stock makes a relative high equal to the most recent relative high. In this a technician sees strong indications that the down trend is at least pausing and possibly ending, and would likely stop actively selling the stock at that point.

History tends to repeat itself

Technical analysts believe that investors collectively repeat the behavior of the investors that preceded them. To a technician, the emotions in the market may be irrational, but they exist. Because investor behavior repeats itself so often, technicians believe that recognizable (and predictable) price patterns will develop on a chart.[9] Recognition of these patterns can allow the technician to select trades that have a higher probability of success.[28]

Technical analysis is not limited to charting, but it always considers price trends.[1] For example, many technicians monitor surveys of investor sentiment. These surveys gauge the attitude of market participants, specifically whether they are bearish or bullish. Technicians use these surveys to help determine whether a trend will continue or if a reversal could develop; they are most likely to anticipate a change when the surveys report extreme investor sentiment.[29] Surveys that show overwhelming bullishness, for example, are evidence that an uptrend may reverse; the premise being that if most investors are bullish they have already bought the market (anticipating higher prices). And because most investors are bullish and invested, one assumes that few buyers remain. This leaves more potential sellers than buyers, despite the bullish sentiment. This suggests that prices will trend down, and is an example of contrarian trading.[30]

Recently, Kim Man Lui, Lun Hu, and Keith C.C. Chan have suggested that there is statistical evidence of association relationships between some of the index composite stocks whereas there is no evidence for such a relationship between some index composite others. They show that the price behavior of these Hang Seng index composite stocks is easier to understand than that of the index.[31]


The industry is globally represented by the International Federation of Technical Analysts (IFTA), which is a federation of regional and national organizations. In the United States, the industry is represented by both the CMT Association and the American Association of Professional Technical Analysts (AAPTA). The United States is also represented by the Technical Security Analysts Association of San Francisco (TSAASF). In the United Kingdom, the industry is represented by the Society of Technical Analysts (STA). The STA was a founding member of IFTA, has recently celebrated its 50th Anniversary and certifies analysts with the Diploma in Technical Analysis. In Canada the industry is represented by the Canadian Society of Technical Analysts.[32] In Australia, the industry is represented by the Australian Technical Analysts Association (ATAA),[33] (which is affiliated to IFTA) and the Australian Professional Technical Analysts (APTA) Inc.[34]

Professional technical analysis societies have worked on creating a body of knowledge that describes the field of Technical Analysis. A body of knowledge is central to the field as a way of defining how and why technical analysis may work. It can then be used by academia, as well as regulatory bodies, in developing proper research and standards for the field. The CMT Association has published a body of knowledge, which is the structure for the Chartered Market Technician (CMT) exam.[35]


Technical analysis software automates the charting, analysis and reporting functions that support technical analysts in their review and prediction of financial markets (e.g. the stock market).

Systematic trading

Neural networks

Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators,[36][37] meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.

As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.[38][39][40]

While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders. However, large-scale application is problematic because of the problem of matching the correct neural topology to the market being studied.


Systematic trading is most often employed after testing an investment strategy on historic data. This is known as backtesting. Backtesting is most often performed for technical indicators, but can be applied to most investment strategies (e.g. fundamental analysis). While traditional backtesting was done by hand, this was usually only performed on human-selected stocks, and was thus prone to prior knowledge in stock selection. With the advent of computers, backtesting can be performed on entire exchanges over decades of historic data in very short amounts of time.

The use of computers does have its drawbacks, being limited to algorithms that a computer can perform. Several trading strategies rely on human interpretation,[41] and are unsuitable for computer processing.[42] Only technical indicators which are entirely algorithmic can be programmed for computerised automated backtesting.

Combination with other market forecast methods

John Murphy states that the principal sources of information available to technicians are price, volume and open interest.[9] Other data, such as indicators and sentiment analysis, are considered secondary.

However, many technical analysts reach outside pure technical analysis, combining other market forecast methods with their technical work. One advocate for this approach is John Bollinger, who coined the term rational analysis in the middle 1980s for the intersection of technical analysis and fundamental analysis.[43] Another such approach, fusion analysis, overlays fundamental analysis with technical, in an attempt to improve portfolio manager performance.

Technical analysis is also often combined with quantitative analysis and economics. For example, neural networks may be used to help identify intermarket relationships.[44]

Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical analysts.[45]

Empirical evidence

Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power.[46] Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult.[13] Nonlinear prediction using neural networks occasionally produces statistically significant prediction results.[47] A Federal Reserve working paper[19] regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions", although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined".

Technical trading strategies were found to be effective in the Chinese marketplace by a recent study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving-average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.50 percent."[48]

An influential 1992 study by Brock et al. which appeared to find support for technical trading rules was tested for data snooping and other problems in 1999;[49] the sample covered by Brock et al. was robust to data snooping.

Subsequently, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: "for the U.S., Japanese and most Western European stock market indices the recursive out-of-sample forecasting procedure does not show to be profitable, after implementing little transaction costs. Moreover, for sufficiently high transaction costs it is found, by estimating CAPMs, that technical trading shows no statistically significant risk-corrected out-of-sample forecasting power for almost all of the stock market indices."[16] Transaction costs are particularly applicable to "momentum strategies"; a comprehensive 1996 review of the data and studies concluded that even small transaction costs would lead to an inability to capture any excess from such strategies.[50]

In a paper published in the Journal of Finance, Dr. Andrew W. Lo, director MIT Laboratory for Financial Engineering, working with Harry Mamaysky and Jiang Wang found that:

Technical analysis, also known as "charting", has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis – the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution – conditioned on specific technical indicators such as head-and-shoulders or double-bottoms – we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.[51]

In that same paper Dr. Lo wrote that "several academic studies suggest that ... technical analysis may well be an effective means for extracting useful information from market prices."[51] Some techniques such as Drummond Geometry attempt to overcome the past data bias by projecting support and resistance levels from differing time frames into the near-term future and combining that with reversion to the mean techniques.[52]

Efficient-market hypothesis

The efficient-market hypothesis (EMH) contradicts the basic tenets of technical analysis by stating that past prices cannot be used to profitably predict future prices. Thus it holds that technical analysis cannot be effective. Economist Eugene Fama published the seminal paper on the EMH in the Journal of Finance in 1970, and said "In short, the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse."[53]

Technicians say that EMH ignores the way markets work, in that many investors base their expectations on past earnings or track record, for example. Because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices.[54] They also point to research in the field of behavioral finance, specifically that people are not the rational participants EMH makes them out to be. Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes.[55] Author David Aronson says that the theory of behavioral finance blends with the practice of technical analysis:

By considering the impact of emotions, cognitive errors, irrational preferences, and the dynamics of group behavior, behavioral finance offers succinct explanations of excess market volatility as well as the excess returns earned by stale information strategies.... cognitive errors may also explain the existence of market inefficiencies that spawn the systematic price movements that allow objective TA [technical analysis] methods to work.[54]

EMH advocates reply that while individual market participants do not always act rationally (or have complete information), their aggregate decisions balance each other, resulting in a rational outcome (optimists who buy stock and bid the price higher are countered by pessimists who sell their stock, which keeps the price in equilibrium).[56] Likewise, complete information is reflected in the price because all market participants bring their own individual, but incomplete, knowledge together in the market.[56]

Random walk hypothesis

The random walk hypothesis may be derived from the weak-form efficient markets hypothesis, which is based on the assumption that market participants take full account of any information contained in past price movements (but not necessarily other public information). In his book A Random Walk Down Wall Street, Princeton economist Burton Malkiel said that technical forecasting tools such as pattern analysis must ultimately be self-defeating: "The problem is that once such a regularity is known to market participants, people will act in such a way that prevents it from happening in the future."[57] Malkiel has stated that while momentum may explain some stock price movements, there is not enough momentum to make excess profits. Malkiel has compared technical analysis to "astrology".[58]

In the late 1980s, professors Andrew Lo and Craig McKinlay published a paper which cast doubt on the random walk hypothesis. In a 1999 response to Malkiel, Lo and McKinlay collected empirical papers that questioned the hypothesis' applicability[59] that suggested a non-random and possibly predictive component to stock price movement, though they were careful to point out that rejecting random walk does not necessarily invalidate EMH, which is an entirely separate concept from RWH. In a 2000 paper, Andrew Lo back-analyzed data from U.S. from 1962 to 1996 and found that "several technical indicators do provide incremental information and may have some practical value".[51] Burton Malkiel dismissed the irregularities mentioned by Lo and McKinlay as being too small to profit from.[58]

Technicians say that the EMH and random walk theories both ignore the realities of markets, in that participants are not completely rational and that current price moves are not independent of previous moves.[27][60] Some signal processing researchers negate the random walk hypothesis that stock market prices resemble Wiener processes, because the statistical moments of such processes and real stock data vary significantly with respect to window size and similarity measure.[61] They argue that feature transformations used for the description of audio and biosignals can also be used to predict stock market prices successfully which would contradict the random walk hypothesis.

The random walk index (RWI) is a technical indicator that attempts to determine if a stock’s price movement is random in nature or a result of a statistically significant trend. The random walk index attempts to determine when the market is in a strong uptrend or downtrend by measuring price ranges over N and how it differs from what would be expected by a random walk (randomly going up or down). The greater the range suggests a stronger trend.[62]

Scientific technical analysis

Caginalp and Balenovich in 1994[63] used their asset-flow differential equations model to show that the major patterns of technical analysis could be generated with some basic assumptions. Some of the patterns such as a triangle continuation or reversal pattern can be generated with the assumption of two distinct groups of investors with different assessments of valuation.The major assumptions of the models are that the finiteness of assets and the use of trend as well as valuation in decision making. Many of the patterns follow as mathematically logical consequences of these assumptions.

One of the problems with conventional technical analysis has been the difficulty of specifying the patterns in a manner that permits objective testing.

Japanese candlestick patterns involve patterns of a few days that are within an uptrend or downtrend. Caginalp and Laurent[64] were the first to perform a successful large scale test of patterns. A mathematically precise set of criteria were tested by first using a definition of a short term trend by smoothing the data and allowing for one deviation in the smoothed trend. They then considered eight major three-day candlestick reversal patterns in a non-parametric manner and defined the patterns as a set of inequalities. The results were positive with an overwhelming statistical confidence for each of the patterns using the data set of all S&P 500 stocks daily for the five-year period 1992-1996.

Among the most basic ideas of conventional technical analysis is that a trend, once established, tends to continue. However, testing for this trend has often led researchers to conclude that stocks are a random walk. One study, performed by Poterba and Summers,[65] found a small trend effect that was too small to be of trading value. As Fisher Black noted,[66] "noise" in trading price data makes it difficult to test hypotheses.

One method for avoiding this noise was discovered in 1995 by Caginalp and Constantine[67] who used a ratio of two essentially identical closed-end funds to eliminate any changes in valuation. A closed-end fund (unlike an open-end fund) trades independently of its net asset value and its shares cannot be redeemed, but only traded among investors as any other stock on the exchanges. In this study, the authors found that the best estimate of tomorrow's price is not yesterday's price (as the efficient-market hypothesis would indicate), nor is it the pure momentum price (namely, the same relative price change from yesterday to today continues from today to tomorrow). But rather it is almost exactly halfway between the two.

Starting from the characterization of the past time evolution of market prices in terms of price velocity and price acceleration, an attempt towards a general framework for technical analysis has been developed, with the goal of establishing a principled classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time translational invariant properties.[68] The classification relies on two dimensionless parameters, the Froude number characterizing the relative strength of the acceleration with respect to the velocity and the time horizon forecast dimensionalized to the training period. Trend-following and contrarian patterns are found to coexist and depend on the dimensionless time horizon. Using a renormalisation group approach, the probabilistic based scenario approach exhibits statistically signifificant predictive power in essentially all tested market phases.

A survey of modern studies by Park and Irwin[69] showed that most found a positive result from technical analysis.

In 2011, Caginalp and DeSantis[70] have used large data sets of closed-end funds, where comparison with valuation is possible, in order to determine quantitatively whether key aspects of technical analysis such as trend and resistance have scientific validity. Using data sets of over 100,000 points they demonstrate that trend has an effect that is at least half as important as valuation. The effects of volume and volatility, which are smaller, are also evident and statistically significant. An important aspect of their work involves the nonlinear effect of trend. Positive trends that occur within approximately 3.7 standard deviations have a positive effect. For stronger uptrends, there is a negative effect on returns, suggesting that profit taking occurs as the magnitude of the uptrend increases. For downtrends the situation is similar except that the "buying on dips" does not take place until the downtrend is a 4.6 standard deviation event. These methods can be used to examine investor behavior and compare the underlying strategies among different asset classes.

In 2013, Kim Man Lui and T Chong pointed out that the past findings on technical analysis mostly reported the profitability of specific trading rules for a given set of historical data. These past studies had not taken the human trader into consideration as no real-world trader would mechanically adopt signals from any technical analysis method. Therefore, to unveil the truth of technical analysis, we should get back to understand the performance between experienced and novice traders. If the market really walks randomly, there will be no difference between these two kinds of traders. However, it is found by experiment that traders who are more knowledgeable on technical analysis significantly outperform those who are less knowledgeable.[71]

Ticker-tape reading

Until the mid-1960s, tape reading was a popular form of technical analysis. It consisted of reading market information such as price, volume, order size, and so on from a paper strip which ran through a machine called a stock ticker. Market data was sent to brokerage houses and to the homes and offices of the most active speculators. This system fell into disuse with the advent of electronic information panels in the late 60's, and later computers, which allow for the easy preparation of charts.

Quotation board

Another form of technical analysis used so far was via interpretation of stock market data contained in quotation boards, that in the times before electronic screens, were huge chalkboards located in the stock exchanges, with data of the main financial assets listed on exchanges for analysis of their movements.[72] It was manually updated with chalk, with the updates regarding some of these data being transmitted to environments outside of exchanges (such as brokerage houses, bucket shops, etc.) via the aforementioned tape, telegraph, telephone and later telex.[73]

This analysis tool was used both, on the spot, mainly by market professionals for day trading and scalping, as well as by general public through the printed versions in newspapers showing the data of the negotiations of the previous day, for swing and position trades.[74]

Charting terms and indicators


  • Average true range – averaged daily trading range, adjusted for price gaps.
  • Breakout – the concept whereby prices forcefully penetrate an area of prior support or resistance, usually, but not always, accompanied by an increase in volume.
  • Chart pattern – distinctive pattern created by the movement of security or commodity prices on a chart
  • Cycles – time targets for potential change in price action (price only moves up, down, or sideways)
  • Dead cat bounce – the phenomenon whereby a spectacular decline in the price of a stock is immediately followed by a moderate and temporary rise before resuming its downward movement
  • Elliott wave principle and the golden ratio to calculate successive price movements and retracements
  • Fibonacci ratios – used as a guide to determine support and resistance
  • Momentum – the rate of price change
  • Point and figure analysis – A priced-based analytical approach employing numerical filters which may incorporate time references, though ignores time entirely in its construction
  • Resistance – a price level that may prompt a net increase of selling activity
  • Support – a price level that may prompt a net increase of buying activity
  • Trending – the phenomenon by which price movement tends to persist in one direction for an extended period of time

Types of charts

  • Candlestick chart – Of Japanese origin and similar to OHLC, candlesticks widen and fill the interval between the open and close prices to emphasize the open/close relationship. In the West, often black or red candle bodies represent a close lower than the open, while white, green or blue candles represent a close higher than the open price.
  • Line chart – Connects the closing price values with line segments. You can also choose to draw the line chart using open, high or low price.
  • Open-high-low-close chart – OHLC charts, also known as bar charts, plot the span between the high and low prices of a trading period as a vertical line segment at the trading time, and the open and close prices with horizontal tick marks on the range line, usually a tick to the left for the open price and a tick to the right for the closing price.
  • Point and figure chart – a chart type employing numerical filters with only passing references to time, and which ignores time entirely in its construction.


Overlays are generally superimposed over the main price chart.

  • Bollinger bands – a range of price volatility
  • Channel – a pair of parallel trend lines
  • Ichimoku kinko hyo – a moving average-based system that factors in time and the average point between a candle's high and low
  • Moving average – an average over a window of time before and after a given time point that is repeated at each time point in the given chart. A moving average can be thought of as a kind of dynamic trend-line.
  • Parabolic SAR – Wilder's trailing stop based on prices tending to stay within a parabolic curve during a strong trend
  • Pivot point – derived by calculating the numerical average of a particular currency's or stock's high, low and closing prices
  • Resistance – a price level that may act as a ceiling above price
  • Support – a price level that may act as a floor below price
  • Trend line – a sloping line described by at least two peaks or two troughs
  • Zig Zag – This chart overlay that shows filtered price movements that are greater than a given percentage.

Breadth indicators

These indicators are based on statistics derived from the broad market.

Price-based indicators

These indicators are generally shown below or above the main price chart.

Volume-based indicators

Trading with Mixing Indicators

See also


  1. ^ a b Kirkpatrick & Dahlquist (2006), p. 3
  2. ^ Akston, Dr. Hugh (13 January 2009). "Beating the Quants at Their Own Game".
  3. ^ Mizrach, Bruce; Weerts, Susan (27 November 2007). "Highs and Lows: A Behavioral and Technical Analysis". SSRN 1118080.
  4. ^ Paul V. Azzopardi (2010). Behavioural Technical Analysis: An introduction to behavioural finance and its role in technical analysis. Harriman House. ISBN 978-1905641413.
  5. ^ Andrew W. Lo; Jasmina Hasanhodzic (2010). The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals. Bloomberg Press. p. 150. ISBN 978-1576603499. Retrieved 8 August 2011.
  6. ^ Joseph de la Vega, Confusión de Confusiones, 1688
  7. ^ Nison, Steve (1991). Japanese Candlestick Charting Techniques. pp. 15–18. ISBN 978-0-13-931650-0.
  8. ^ Nison, Steve (1994). Beyond Candlesticks: New Japanese Charting Techniques Revealed, John Wiley and Sons, p. 14. ISBN 0-471-00720-X
  9. ^ a b c d Murphy, John J. Technical Analysis of the Financial Markets. New York Institute of Finance, 1999, pp. 1-5, 24-31. ISBN 0-7352-0066-1
  10. ^ " Head and Shoulders Pattern". Archived from the original on 2015-01-06. Retrieved 2015-01-06.
  11. ^ Elder (1993), Part III: Classical Chart Analysis
  12. ^ Elder (1993), Part II: "Mass Psychology"; Chapter 17: "Managing versus Forecasting", pp. 65–68
  13. ^ a b Irwin, Scott H. and Park, Cheol-Ho. (2007). "What Do We Know About the Profitability of Technical Analysis?" Journal of Economic Surveys, Vol. 21, No. 4, pp. 786-826. Available at SSRN. doi:10.1111/j.1467-6419.2007.00519.x.
  14. ^ Paulos, J.A. (2003). A Mathematician Plays the Stock Market. Basic Books.
  15. ^ Fama, Eugene (May 1970). "Efficient Capital Markets: A Review of Theory and Empirical Work," The Journal of Finance, v. 25 (2), pp. 383-417.
  16. ^ a b Griffioen, Technical Analysis in Financial Markets
  17. ^ Schwager, Jack D. Getting Started in Technical Analysis. Wiley, 1999, p. 2. ISBN 0-471-29542-6
  18. ^ Brock, William; Lakonishok, Josef; Lebaron, Blake (1992). "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns". The Journal of Finance. 47 (5): 1731–1764. CiteSeerX doi:10.2307/2328994. JSTOR 2328994.
  19. ^ a b Osler, Karen (July 2000). "Support for Resistance: Technical Analysis and Intraday Exchange Rates," FRBNY Economic Policy Review (abstract and paper here).
  20. ^ Neely, Christopher J., and Paul A. Weller (2001). "Technical analysis and Central Bank Intervention," Journal of International Money and Finance, 20 (7), 949–70 (abstract and paper here)
  21. ^ Taylor, M.P.; Allen, H. (1992). "The use of technical analysis in the foreign exchange market". Journal of International Money and Finance. 11 (3): 304–314. doi:10.1016/0261-5606(92)90048-3. Retrieved 2008-03-29.
  22. ^ Frankel, J.A.; Froot, K.A. (1990). "Chartists, Fundamentalists, and Trading in the Foreign Exchange Market". The American Economic Review. 80 (2): 181–185. JSTOR 2006566.
  23. ^ Neely, C.J (1998). "Technical Analysis and the Profitability of US Foreign Exchange Intervention". Federal Reserve Bank of St. Louis Review. 80 (4): 3–17. Retrieved 2008-03-29.
  24. ^ Lento, Camillo (2008). "A Combined Signal Approach to Technical Analysis on the S&P 500". Journal of Business & Economics Research. 6 (8): 41–51.
  25. ^ Elder (2008), Chapter 1 - section "Trend vs Counter-Trending Trading"
  26. ^ "Beware of the Stock Market as a Self-Fulfilling Prophecy".
  27. ^ a b Kahn, Michael N. (2006). Technical Analysis Plain and Simple: Charting the Markets in Your Language, Financial Times Press, Upper Saddle River, New Jersey, p. 80. ISBN 0-13-134597-4.
  28. ^ Baiynd, Anne-Marie (2011). The Trading Book: A Complete Solution to Mastering Technical Systems and Trading Psychology. McGraw-Hill. p. 272. ISBN 9780071766494. Archived from the original on 2012-03-25. Retrieved 2013-04-30.
  29. ^ Kirkpatrick & Dahlquist (2006), p. 87
  30. ^ Kirkpatrick & Dahlquist (2006), p. 86
  31. ^ Kim Man Lui, Lun Hu, and Keith C.C. Chan. "Discovering Pattern Associations in Hang Seng Index Constituent Stocks", International Journal of Economics and Finance, Vol. 2, No. 2 (2010)
  32. ^ Technical Analysis: The Complete Resource for Financial Market Technicians, p. 7
  33. ^ "Home - Australian Technical Analysts Association".
  34. ^ "Home".
  35. ^ "CMT Association Knowledge Base".
  36. ^ K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks vol 2, 1989
  37. ^ K. Hornik, Multilayer feed-forward networks are universal approximators, Neural Networks, vol 2, 1989
  38. ^ R. Lawrence. Using Neural Networks to Forecast Stock Market Prices
  39. ^ B.Egeli et al. Stock Market Prediction Using Artificial Neural Networks Archived 2007-06-20 at the Wayback Machine
  40. ^ M. Zekić. Neural Network Applications in Stock Market Predictions - A Methodology Analysis Archived 2012-04-24 at the Wayback Machine
  41. ^ Elder (1993), pp. 54, 116–118
  42. ^ Elder (1993)
  43. ^ ltd, Research and Markets. "The Capital Growth Letter - Research and Markets".
  44. ^
  45. ^ "SFO". Archived from the original on 2007-10-06. Retrieved 2007-08-27.
  46. ^ Browning, E.S. (July 31, 2007). "Reading market tea leaves". The Wall Street Journal Europe. Dow Jones. pp. 17–18.
  47. ^ Skabar, Cloete, Networks, Financial Trading and the Efficient Markets Hypothesis
  48. ^ Nauzer J. Balsara, Gary Chen and Lin Zheng "The Chinese Stock Market: An Examination of the Random Walk Model and Technical Trading Rules" The Quarterly Journal of Business and Economics, Spring 2007
  49. ^ Sullivan, R.; Timmermann, A.; White, H. (1999). "Data-Snooping, Technical Trading Rule Performance, and the Bootstrap". The Journal of Finance. 54 (5): 1647–1691. CiteSeerX doi:10.1111/0022-1082.00163.
  50. ^ Chan, L.K.C.; Jegadeesh, N.; Lakonishok, J. (1996). "Momentum Strategies". The Journal of Finance. 51 (5): 1681–1713. doi:10.2307/2329534. JSTOR 2329534.
  51. ^ a b c Lo, Andrew W.; Mamaysky, Harry; Wang, Jiang (2000). "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation". Journal of Finance. 55 (4): 1705–1765. CiteSeerX doi:10.1111/0022-1082.00265.
  52. ^ David Keller, "Breakthroughs in Technical Analysis; New Thinking from the World's Top Minds," New York, Bloomberg Press, 2007, ISBN 978-1-57660-242-3 pp.1-19
  53. ^ Eugene Fama, "Efficient Capital Markets: A Review of Theory and Empirical Work," The Journal of Finance, volume 25, issue 2 (May 1970), pp. 383-417.
  54. ^ a b Aronson, David R. (2006). Evidence-Based Technical Analysis, Hoboken, New Jersey: John Wiley and Sons, pages 357, 355-356, 342. ISBN 978-0-470-00874-4.
  55. ^ Prechter, Robert R, Jr; Parker, Wayne D (2007). "The Financial/Economic Dichotomy in Social Behavioral Dynamics: The Socionomic Perspective". Journal of Behavioral Finance. 8 (2): 84–108. CiteSeerX doi:10.1080/15427560701381028.CS1 maint: Multiple names: authors list (link)
  56. ^ a b Clarke, J., T. Jandik, and Gershon Mandelker (2001). "The efficient markets hypothesis," Expert Financial Planning: Advice from Industry Leaders, ed. R. Arffa, 126-141. New York: Wiley & Sons.
  57. ^ Burton Malkiel, A Random Walk Down Wall Street, W. W. Norton & Company (April 2003) p. 168.
  58. ^ a b Robert Huebscher. Burton Malkiel Talks the Random Walk. July 7, 2009.
  59. ^ Lo, Andrew; MacKinlay, Craig. A Non-Random Walk Down Wall Street, Princeton University Press, 1999. ISBN 978-0-691-05774-3
  60. ^ Poser, Steven W. (2003). Applying Elliott Wave Theory Profitably, John Wiley and Sons, p. 71. ISBN 0-471-42007-7.
  61. ^ Eidenberger, Horst (2011). "Fundamental Media Understanding" Atpress. ISBN 978-3-8423-7917-6.
  62. ^ " Trading Indicator Glossary". Archived from the original on 2011-09-01. Retrieved 2011-08-01.
  63. ^ Gunduz Caginalp & Donald Balenovich (2003). "A theoretical foundation for technical analysis" (PDF). Journal of Technical Analysis. 59: 5–22.CS1 maint: Uses authors parameter (link)
  64. ^ G. Caginalp and H. Laurent, "The Predictive Power of Price Patterns." Applied Mathematical Finance, Vol. 5, pp. 181-206, 1998.
  65. ^ J.M. Poterba and L.H. Summers, "Mean reversion in stock prices: Evidence and Implications," Journal of Financial Economics 22, 27-59, 1988.
  66. ^ Black, F. 1986. Noise. Journal of Finance 41:529-43.
  67. ^ G. Caginalp and G. Constantine, "Statistical inference and modeling of momentum in stock prices," Applied Mathematical Finance 2, 225-242, 1995.
  68. ^ J. V. Andersen, S. Gluzman and D. Sornette, Fundamental Framework for Technical Analysis, European Physical Journal B 14, 579-601 (2000)
  69. ^ C-H Park and S.H. Irwin, "The Profitability of Technical Analysis: A Review" AgMAS Project Research Report No. 2004-04
  70. ^ G. Caginalp and M. DeSantis, "Nonlinearity in the dynamics of financial markets," Nonlinear Analysis: Real World Applications, 12(2), 1140-1151, 2011.
  71. ^ K.M. Lui and T.T.L Chong, "Do Technical Analysts Outperform Novice Traders: Experimental Evidence" Economics Bulletin. 33(4), 3080-3087, 2013.
  72. ^ Lefèvre (2000), pp. 1, 18
  73. ^ Lefèvre (2000), p. 17
  74. ^ Livermore; Jesse "How to Trade in Stocks" Duell, Sloan & Pearce NY 1940, pp. 17-18


Further reading

  • Colby, Robert W. The Encyclopedia of Technical Market Indicators. 2nd Edition. McGraw Hill, 2003. ISBN 0-07-012057-9
  • Covel, Michael. The Complete Turtle Trader. HarperCollins, 2007. ISBN 9780061241703
  • Douglas, Mark. The Disciplined Trader. New York Institute of Finance, 1990. ISBN 0-13-215757-8
  • Edwards, Robert D.; Magee, John; Bassetti, W.H.C. Technical Analysis of Stock Trends, 9th Edition (Hardcover). American Management Association, 2007. ISBN 0-8493-3772-0
  • Fox, Justin. The Myth of the Rational Market. HarperCollings, 2009. ISBN 9780060598990
  • Hurst, J. M. The Profit Magic of Stock Transaction Timing. Prentice-Hall, 1972. ISBN 0-13-726018-0
  • Neill, Humphrey B. Tape Reading & Market Tactics. First edition of 1931. Market Place 2007 reprint ISBN 1592802621
  • Neill, Humphrey B. The Art of Contrary Thinking. Caxton Press 1954.
  • Pring, Martin J. Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points. McGraw Hill, 2002. ISBN 0-07-138193-7
  • Raschke, Linda Bradford; Connors, Lawrence A. Street Smarts: High Probability Short-Term Trading Strategies. M. Gordon Publishing Group, 1995. ISBN 0-9650461-0-9
  • Rollo Tape & Wyckoff, Richard D. Studies in Tape Reading The Ticker Publishing Co. NY 1910.
  • Tharp, Van K. Definitive Guide to Position Sizing International Institute of Trading Mastery, 2008. ISBN 0935219099
  • Wilder, J. Welles. New Concepts in Technical Trading Systems. Trend Research, 1978. ISBN 0-89459-027-8
  • Ladis Konecny, Stocks and Exchange – the only Book you need, 2013, ISBN 9783848220656, technical analysis = chapter 8.
  • Schabackers, Richard W. Stock Market Theory and Practice, 2011. ISBN 9781258159474

External links

International and national organizations
Bottom (technical analysis)

In the technical analysis of security prices, a bottom is a chart pattern where prices reach a low, then a lower low, and then a higher low.

According to some technical analysis theories, the first low signifies the pressure from selling was greater than the pressure from buying. The second lower low suggests that selling still had more pressure than the buying. The third higher low suggests that buying pressure will not let prices fall as low as the previous low. This turning point from selling pressure to buying pressure is called a bottom.

Breakout (technical analysis)

A breakout is when prices pass through and stay through an area of support or resistance. On the technical analysis chart a break out occurs when price of a stock or commodity exits an area pattern. Oftentimes, a stock or commodity will bounce between the areas of support and resistance and when it breaks through either one of these barriers you can consider the direction that it's heading in a trend. Often the resistance level the price breaks through becomes a new support level, and vice versa. This can be a "Buy" or "Sell" signal depending on which barrier it broke through.

Capital gain

A capital gain refers to profit that results from a sale of a capital asset, such as stock, bond or real estate, where the sale price exceeds the purchase price. The gain is the difference between a higher selling price and a lower purchase price. Conversely, a capital loss arises if the proceeds from the sale of a capital asset are less than the purchase price.

Capital gains may also refer to a different form of profit received from an asset which refers to "investment income" in the form of cash flow or passive income that arises in relation to real assets, such as property; financial assets, such as shares/stocks or bonds; and intangible assets.

Chart pattern

A chart pattern or price pattern is a pattern within a chart when prices are graphed. In stock and commodity markets trading, chart pattern studies play a large role during technical analysis. When data is plotted there is usually a pattern which naturally occurs and repeats over a period. Chart patterns are used as either reversal or continuation signals.

There are 3 main types of chart pattern which are currently used by technical analysts : traditional chart pattern, harmonic pattern, and candlestick pattern.

Dead cat bounce

In finance, a dead cat bounce is a small, brief recovery in the price of a declining stock. Derived from the idea that "even a dead cat will bounce if it falls from a great height", the phrase, which originated on Wall Street, is also popularly applied to any case where a subject experiences a brief resurgence during or following a severe decline.

Elliott wave principle

The Elliott wave principle is a form of technical analysis that finance traders use to analyze financial market cycles and forecast market trends by identifying extremes in investor psychology, highs and lows in prices, and other collective factors. Ralph Nelson Elliott (1871–1948), a professional accountant, discovered the underlying social principles and developed the analytical tools in the 1930s. He proposed that market prices unfold in specific patterns, which practitioners today call "Elliott waves", or simply "waves". Elliott published his theory of market behavior in the book The Wave Principle in 1938, summarized it in a series of articles in Financial World magazine in 1939, and covered it most comprehensively in his final major work, Nature's Laws: The Secret of the Universe in 1946. Elliott stated that "because man is subject to rhythmical procedure, calculations having to do with his activities can be projected far into the future with a justification and certainty heretofore unattainable." The empirical validity of the Elliott wave principle remains the subject of debate.

Fibonacci retracement

In finance, Fibonacci retracement is a method of technical analysis for determining support and resistance levels. They are named after their use of the Fibonacci sequence. Fibonacci retracement is based on the idea that markets will retrace a predictable portion of a move, after which they will continue to move in the original direction.

The appearance of retracement can be ascribed to ordinary price volatility as described by Burton Malkiel, a Princeton economist in his book A Random Walk Down Wall Street, who found no reliable predictions in technical analysis methods taken as a whole. Malkiel argues that asset prices typically exhibit signs of random walk and that one cannot consistently outperform market averages.

Fibonacci retracement is created by taking two extreme points on a chart and dividing the vertical distance by the key Fibonacci ratios. 0.0% is considered to be the start of the retracement, while 100.0% is a complete reversal to the original part of the move. Once these levels are identified, horizontal lines are drawn and used to identify possible support and resistance levels (see trend line). The significance of such levels, however, could not be confirmed by examining the data. Arthur Merrill in Filtered Waves determined there is no reliably standard retracement: not 50%, 23.6%, 38.2%, 61.8%, nor any other.

Gap (chart pattern)

A gap is defined as an unfilled space or interval. On a technical analysis chart, a gap represents an area where no trading takes place. On the Japanese candlestick chart, a window is interpreted as a gap.

In an upward trend, a gap is produced when the highest price of one day is lower than the lowest price of the following day. Conversely, in a downward trend, a gap occurs when the lowest price of any one day is higher than the highest price of the next day.

For example, the price of a share reaches a high of $30.00 on Wednesday, and opens at $31.20 on Thursday, falls down to $31.00 in the early hour, moves straight up again to $31.45, and no trading occurs in between $30.00 and $31.00 area. This no-trading zone appears on the chart as a gap.

Gaps can play an important role when spotted before the beginning of a move.

Head and shoulders (chart pattern)

On the technical analysis chart, the Head and shoulders formation occurs when a market trend is in the process of reversal either from a bullish or bearish trend; a characteristic pattern takes shape and is recognized as reversal formation.

Momentum (finance)

In finance, momentum is the empirically observed tendency for rising asset prices to rise further, and falling prices to keep falling. For instance, it was shown that stocks with strong past performance continue to outperform stocks with poor past performance in the next period with an average excess return of about 1% per month. Momentum signals (e.g., 52-week high) have been shown to be used by financial analysts in their buy and sell recommendations.The existence of momentum is a market anomaly, which finance theory struggles to explain. The difficulty is that an increase in asset prices, in and of itself, should not warrant further increase. Such increase, according to the efficient-market hypothesis, is warranted only by changes in demand and supply or new information (cf. fundamental analysis). Students of financial economics have largely attributed the appearance of momentum to cognitive biases, which belong in the realm of behavioral economics. The explanation is that investors are irrational, in that they underreact to new information by failing to incorporate news in their transaction prices. However, much as in the case of price bubbles, recent research has argued that momentum can be observed even with perfectly rational traders.

Moving average

In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter. Variations include: simple, and cumulative, or weighted forms (described below).

Given a series of numbers and a fixed subset size, the first element of the moving average is obtained by taking the average of the initial fixed subset of the number series. Then the subset is modified by "shifting forward"; that is, excluding the first number of the series and including the next value in the subset.

A moving average is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set accordingly. For example, it is often used in technical analysis of financial data, like stock prices, returns or trading volumes. It is also used in economics to examine gross domestic product, employment or other macroeconomic time series. Mathematically, a moving average is a type of convolution and so it can be viewed as an example of a low-pass filter used in signal processing. When used with non-time series data, a moving average filters higher frequency components without any specific connection to time, although typically some kind of ordering is implied. Viewed simplistically it can be regarded as smoothing the data.

Pivot point (technical analysis)

In financial markets, a pivot point is a price level that is used by traders as a possible indicator of market movement. A pivot point is calculated as an average of significant prices (high, low, close) from the performance of a market in the prior trading period. If the market in the following period trades above the pivot point it is usually evaluated as a bullish sentiment, whereas trading below the pivot point is seen as bearish.

It is customary to calculate additional levels of support and resistance, below and above the pivot point, respectively, by subtracting or adding price differentials calculated from previous trading ranges of the market.A pivot point and the associated support and resistance levels are often turning points for the direction of price movement in a market. In an up-trending market, the pivot point and the resistance levels may represent a ceiling level in price above which the uptrend is no longer sustainable and a reversal may occur. In a declining market, a pivot point and the support levels may represent a low price level of stability or a resistance to further decline.

Support and resistance

In stock market technical analysis, support and resistance are certain predetermined levels of the price of a security at which it is thought that the price will tend to stop and reverse. These levels are denoted by multiple touches of price without a breakthrough of the level.

Swing trading

Swing trading is a speculative trading strategy in financial markets where a tradable asset is held for between one and several days in an effort to profit from price changes or 'swings'. A swing trading position is typically held longer than a day trading position, but shorter than buy and hold investment strategies that can be held for months or years. Profits can be sought by either buying an asset or short selling. Momentum signals (e.g., 52-week high/low) have been shown to be used by financial analysts in their buy and sell recommendations that can be applied in swing trading.

Top (technical analysis)

In technical analysis, a top is an event in which a security's market price reaches a high, then a higher high, and then a lower high.

The first high signifies the pressure from buying was greater than the pressure from selling. The second higher high suggests that buying still had more pressure than the selling. The third lower high suggests that selling pressure will not let prices rise as high as the previous high. This turning point from buying pressure to selling pressure is called a top.

Trend line (technical analysis)

In finance, a trend line is a bounding line for the price movement of a security. It is formed when a diagonal line can be drawn between a minimum of three or more price pivot points. A line can be drawn between any two points, but it does not qualify as a trend line until tested. Hence the need for the third point, the test. Trend lines are commonly used to decide entry and exit timing when trading securities. They can also be referred to as a Dutch line, as the concept was first used in Holland.

A support trend line is formed when a securities price decreases and then rebounds at a pivot point that aligns with at least two previous support pivot points. Similarly a resistance trend line is formed when a securities price increases and then rebounds at a pivot point that aligns with at least two previous resistance pivot points. Stock often begin or end trending because of a stock catalyst such as a product launch or change in management.

Trend lines are a simple and widely used technical analysis approach to judging entry and exit investment timing. To establish a trend line historical data, typically presented in the format of a chart such as the above price chart, is required. Historically, trend lines have been drawn by hand on paper charts, but it is now more common to use charting software that enables trend lines to be drawn on computer based charts. There are some charting software that will automatically generate trend lines, however most traders prefer to draw their own trend lines.

When establishing trend lines it is important to choose a chart based on a price interval period that aligns with your trading strategy. Short term traders tend to use charts based on interval periods, such as 1 minute (i.e. the price of the security is plotted on the chart every 1 minute), with longer term traders using price charts based on hourly, daily, weekly and monthly interval periods.

However, time periods can also be viewed in terms of years. For example, below is a chart of the S&P 500 since the earliest data point until April 2008. While the Oracle example above uses a linear scale of price changes, long term data is more often viewed as logarithmic: e.g. the changes are really an attempt to approximate percentage changes than pure numerical value.

Trend lines are typically used with price charts, however they can also be used with a range of technical analysis charts such as MACD and RSI. Trend lines can be used to identify positive and negative trending charts, whereby a positive trending chart forms an upsloping line when the support and the resistance pivots points are aligned, and a negative trending chart forms a downsloping line when the support and resistance pivot points are aligned.

Trend lines are used in many ways by traders. If a stock price is moving between support and resistance trend lines, then a basic investment strategy commonly used by traders, is to buy a stock at support and sell at resistance, then short at resistance and cover the short at support. The logic behind this, is that when the price returns to an existing principal trend line it may be an opportunity to open new positions in the direction of the trend, in the belief that the trend line will hold and the trend will continue further. A second way is that when price action breaks through the principal trend line of an existing trend, it is evidence that the trend may be going to fail, and a trader may consider trading in the opposite direction to the existing trend, or exiting positions in the direction of the trend.

Triangle (chart pattern)

Triangles within technical analysis are chart patterns commonly found in the price charts of financially traded assets (stocks, bonds, futures, etc.). The pattern derives its name from the fact that it is characterized by a contraction in price range and converging trend lines, thus giving it a triangular shape.

Triangle patterns can be broken down into three categories: the ascending triangle, the descending triangle, and the symmetrical triangle. While the shape of the triangle is significant, of more importance is the direction that the market moves when it breaks out of the triangle. Lastly, while triangles can sometimes be reversal patterns—meaning a reversal of the prior trend—they are normally seen as continuation patterns (meaning a continuation of the prior trend).

Trix (technical analysis)

Trix (or TRIX) is a technical analysis oscillator developed in the 1980s by Jack Hutson, editor of Technical Analysis of Stocks and Commodities magazine. It shows the slope (i.e. derivative) of a triple-smoothed exponential moving average. The name Trix is from "triple exponential."

Trix is calculated with a given N-day period as follows:

Like any moving average, the triple EMA is just a smoothing of price data and, therefore, is trend-following. A rising or falling line is an uptrend or downtrend and Trix shows the slope of that line, so it's positive for a steady uptrend, negative for a downtrend, and a crossing through zero is a trend-change, i.e. a peak or trough in the underlying average.

The triple-smoothed EMA is very different from a plain EMA. In a plain EMA the latest few days dominate and the EMA follows recent prices quite closely; however, applying it three times results in weightings spread much more broadly, and the weights for the latest few days are in fact smaller than those of days further past. The following graph shows the weightings for an N=10 triple EMA (most recent days at the left):

Note that the distribution's mode will lie with pN-2's weight, i.e. in the graph above p8 carries the highest weighting. An N of 1 is invalid.

The easiest way to calculate the triple EMA based on successive values is just to apply the EMA three times, creating single-, then double-, then triple-smoothed series. The triple EMA can also be expressed directly in terms of the prices as below, with today's close, yesterday's, etc., and with (as for a plain EMA):

The coefficients are the triangle numbers, n(n+1)/2. In theory, the sum is infinite, using all past data, but as f is less than 1 the powers become smaller as the series progresses, and they decrease faster than the coefficients increase, so beyond a certain point the terms are negligible.

Volatility (finance)

In finance, volatility (symbol σ) is the degree of variation of a trading price series over time as measured by the standard deviation of logarithmic returns.

Historic volatility measures a time series of past market prices. Implied volatility looks forward in time, being derived from the market price of a market-traded derivative (in particular, an option).

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