Price Charts, Technical and Fundamental Analysis

A brief summary - strong buy, buy, strong sell, sell or neutral signals for the DAX 30 index. A detailed technical analysis through moving averages buy/sell signals (simple and exponential for 5.

An important aspect of their work involves the nonlinear effect of trend.

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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.

When to trade and when to wait for more favorable conditions? The analytical tools available in the trading platform will help answer this question. There are countless methods of market analysis and trading strategies based on the analytical tools. All of them can be divided into two broad categories: The essence of technical analysis is studying price charts of financial instruments using technical indicators and analytical objects.

Charts in the platform have a variety of settings, so that traders can customize them and adapt to their personal needs. Every chart can display 21 timeframes from one minute M1 to one month MN1. Use 38 built-in technical indicators and an unlimited number of custom indicators available in the Market and Code Base.

The trading platform provides various analytical tools for price analysis: Moreover, in addition to the built-in analysis tools you can download source codes of various free applications from the Code Base.

Thousands of ready-to use applications for technical analysis and automated trading are also available on the Market. The meaning of fundamental analysis is in the constant monitoring and studying of various economic and industrial indicators, which may affect the quotes of a financial instrument. For example, annual report releases, news about a new contract or a regulatory law can seriously affect the price of company shares. 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.

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. Multiple encompasses the psychology generally abounding, i.

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 s and s 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. While some isolated studies have indicated that technical trading rules might lead to consistent returns in the period prior to , [18] [19] [20] [21] most academic work has focused on the nature of the anomalous position of the foreign exchange market.

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. 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. Technical analysts believe that prices trend directionally, i. The basic definition of a price trend was originally put forward by Dow theory.

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. 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. 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.

Technical analysis is not limited to charting, but it always considers price trends. 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. 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. 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. The industry is globally represented by the International Federation of Technical Analysts IFTA , which is a federation of regional and national organizations. 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. Technical analysis software automates the charting, analysis and reporting functions that support technical analysts in their review and prediction of financial markets e. Since the early s 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. 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. 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. 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. John Murphy states that the principal sources of information available to technicians are price, volume and open interest. 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 s for the intersection of technical analysis and fundamental analysis.

Technical analysis is also often combined with quantitative analysis and economics. For example, neural networks may be used to help identify intermarket relationships. Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical analysts. 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. 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.

An influential study by Brock et al. Subsequently, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: 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.

In a paper published in the Journal of Finance , Dr. 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. 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.

In that same paper Dr. Lo wrote that "several academic studies suggest that Thomas DeMark 's indicators enjoy a remarkable endorsement in the financial industry.

Market entry signals have been tested by comparing conditional returns i. For the period from Jan. 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 , and said "In short, the evidence in support of the efficient markets model is extensive, and somewhat uniquely in economics contradictory evidence is sparse. Technicians say [ who? Because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices.

Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes. 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 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.

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: Malkiel has compared technical analysis to " astrology ".

In the late s, professors Andrew Lo and Craig McKinlay published a paper which cast doubt on the random walk hypothesis. In a response to Malkiel, Lo and McKinlay collected empirical papers that questioned the hypothesis' applicability [60] 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 paper, Andrew Lo back-analyzed data from U. 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. Caginalp and Balenovich in [64] 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 [65] 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.

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, [66] found a small trend effect that was too small to be of trading value.

As Fisher Black noted, [67] "noise" in trading price data makes it difficult to test hypotheses.