Interpreting the data
3. Inferring potential future price behavior
Analyzing price and market action is ultimately subjective because all analysis is interpreted through various behavioral traits, filters, and biases unique to each analyst or observer. Behavioral traits include both the psychological and emotional elements. As a consequence, each analyst will possess a slightly different perception of the market and its possible future behavior.
Subjectivity in the Choice of Analysis and Technical Studies
The sheer number of ways to analyze an individual chart contributes to the overall level of subjectivity associated with each forecast. The problem is twofold:
• What is the most appropriate form of technical analysis that should be applied to a particular chart?
• What is the most appropriate choice of indicators to apply to a particular chart?
These are the usual questions that plague novices. The following charts depict the various popular forms of analysis that can be applied to a basic chart of price action. The following examples are by no means exhaustive. Figure 1.9 starts off with a plain chart devoid of any form of analysis.
Figure 1.9 A Simple Price Chart.
Source: MetaTrader 4
The next chart, Figure 1.10, shows the application of basic trendline analysis on the same chart, tracking the flow of price action in the market.
Figure 1.10 Trendline Analysis on the Same Chart.
Source: MetaTrader 4
In Figure 1.11, moving average analysis is now employed to track the same flow of price action and to provide potential points of entry as the market rises and falls.
Figure 1.11 Moving Average Analysis on the Same Chart.
Source: MetaTrader 4
Figure 1.12 depicts the application of chart pattern analysis to track and forecast the shorter-term bullish and bearish movements in price.
Figure 1.12 Chart Pattern Analysis on the Same Chart.
Source: MetaTrader 4
Figure 1.13 is an example of applying two forms of technical analysis, that is, linear regression analysis and divergence analysis to track and forecast potential market tops and bottoms. Notice that the market top coincided perfectly with the upper band of the linear regression line, with an early bearish signal seen in the form of standard bearish divergence on the commodity channel index (CCI) indicator.
Figure 1.13 Linear Regression and Divergence Analysis on the Same Chart.
Source: MetaTrader 4
Figure 1.14 is an example of applying a couple of additional forms of analysis to the basic linear regression band. In this chart, price action analysis is used in conjunction with volume analysis to forecast a potential top in the market, evidenced by the preceding parabolic move in price that is coupled by a blow-off.
Figure 1.14 Linear Regression and Volume Analysis on the Same Chart.
Source: MetaTrader 4
In Figure 1.15, volatility band, volume, and overextension analysis are all employed to seek out potential reversals in the market. We observe that price exceeds the upper volatility band, which may potentially be an early indication of price exhaustion, especially since it is accompanied by a significant volume spike. The moving average convergence-divergence (MACD) indicator is also seen to be residing at historically overbought levels, which is another potentially bearish indication.
Figure 1.15 Volatility Band, Volume, and Overextension Analysis on the Same Chart.
Source: MetaTrader 4
As we can see from just a few forms of analysis presented in the preceding charts, there are many ways to view the action of the markets, depending on the context of the analysis employed. For example, if the analyst is more interested in viewing and understanding the action of price within the context of over-reaction or price exhaustion in the markets, he or she may opt to apply technical studies that track levels or areas of potential over-reaction or price exhaustion. Technical studies that tract such behavior include linear regression bands, Bollinger bands, moving average percentage bands, Keltner and Starc bands, areas of prior support and resistance, and so on. Alternatively, if the analyst is more interested in viewing and understanding the action of price within the context of market momentum, he or she may instead opt to apply breakout analysis of chart patterns, trendlines, moving averages, and so on. As long as the reason for using a particular form of analysis is clear, there should be no confusion as to what the studies are indicating.
Contradictory, Confirmatory, and Complementary Signals
There are many instances when two oscillator signals are in clear and direct opposition with each other. This is inevitable, as each oscillator is constructed differently. The mathematics underlying each oscillator varies with the purpose it is designed for, and in most cases, it involves the manipulation of price, volume, and open interest data. A few reasons for conflicting oscillator and indicator signals are:
• The mathematical construction of each oscillator or indicator is different.
• Each oscillator or indicator tracks a different time horizon.
• Two identical oscillators may issue inconsistent readings due to missing data on one of the charting platforms.
• Two identical oscillators may also issue inconsistent readings due to variations in the accuracy, quality, and type of data available on different charting platforms.
For example, applying an oscillator that uses price, volume, and open interest as part of its calculation will yield inconsistent readings should one of the data be unavailable on the charting platform. The analysts may not be aware of the missing data and struggle to make sense of the inconsistency. The accuracy of the data is also of paramount importance for effective analysis of price and market action. Dropouts in the data as well as the inclusion or exclusion of non-trading days will cause inconsistent readings between charting platforms. There may also be variations in the oscillator readings should volume be replaced with tick volume, sometime also referred to as transaction volume. Tick volume tracks the number of transactions over a specified time interval, irrespective of the size of the transactions.
It is also important to note that conflicting signals may not always be in fact conflicting. As pointed out, the time horizons over which each signal is applied may
