The Handbook of Technical Analysis + Test Bank. Lim Mark Andrew

Give examples of the primary trend being manipulated.

      3. Is Dow Theory still relevant in today’s market?

      4. Describe the main weaknesses of Dow Theory.

      5. Explain why volume should expand in the direction of the existing trend.

      6. In what ways is accumulation similar to distribution?

      7. Why is the secondary reaction more problematic than the primary trend?

      8. What are the main differences between Dow’s and Ralph N. Elliott’s determination of a trend?

      REFERENCES

      W. P Hamilton. 2006. The Stock Market Barometer. New York: Cosimo Classics.

      S. A. Nelson. 2007. The ABC of Stock Speculation. Marketplace Books.

      Robert Rhea. 1994. The Dow Theory. Flint Hill, VA: Fraser Publishing Co.

      Richard Schabacker. 1997. Technical Analysis and Stock Market Profits. Upper Saddle River, NJ: FT Press.

      Jack Schannep. 2008. Dow Theory for the 21st Century: Technical Indicators for Improving Your Investment Results. Hoboken, NJ: John Wiley & Sons.

      CHAPTER 3

      Mechanics and Dynamics of Charting

LEARNING OBJECTIVES

      After studying this chapter, you should be able to:

      • Understand chart construction and how technical data is incorporated and displayed

      • Describe the process by which OHLC data is created and its relationship to various charts

      • Identify and differentiate between contango and backwardation and understand the connection with negative and positive roll yields

      • Understand the adverse effects of the bid-ask spread on trading performance

      • Construct various charts using constant measures of time, range, volatility, trade volume, and number of transactions

      • Set up a volatility-neutral chart for consistent viewing of price action

      Traditional charting is a two-dimensional matrix upon which technical data or information is viewed. It affords the practitioner a means of tracking technical data in a meaningful way, revealing various repetitive price pattern behavioral traits and market volatility. In addition, charting also clearly reveals price distortions and illiquidity in the market. It allows for the application of technical analysis such as the drawing of trendlines, channels, envelopes, and chart patterns on price, helping to uncover important price reaction levels, which are driven by the consistent underlying psychology and perception of all market participants. In this chapter, we shall cover the basics of chart construction and how technical data is displayed.

      3.1 THE MECHANICS AND DYNAMICS OF CHARTING

      There are many ways that a technical analyst can analyze and display market data. Data may be displayed either in a numerical or graphical form. All numerical data may be displayed graphically, if required. Analysts using numerical information to study market action may also resort to various quantitative and statistical techniques in an attempt to predict future price direction and volatility. These quantitative analysts and statisticians employ various forms of time series and stochastic analysis and conduct back and forward testing on technical data. They also try to identify price anomalies and arbitrage opportunities using sophisticated software programs and high-speed data connectivity.

      More traditional analysts prefer to work using only a graphical representation of technical data, which comes in the usual form of a price-time chart, where the vertical axis tracks the movement of price and the horizontal axis tracks the motion of time. The price axis may be scaled in an arithmetic (linear) or logarithmic (ratio) fashion. On some charts, the time axis may not always be plotted in equal increments or units of time, but rather acting more as a counter for new blocks of data rather than an explicit representation of the passing of time. On such charts, time is regarded as implicit along the x axis.

      Traditional analysts study classical chart patterns, trendlines, window oscillators, overlay indicators, and various other price formations. Analysts who use charts to study technical data are called chartists. Note that quantitative analysts and statisticians also tend to use charts to display numerical data, although it is optional.

       Technical Data

      Some of the more common technical data or information employed to construct charts include:

      • Price Data:

      • Open (O)

      • High (H)

      • Low (L)

      • Close (C)

      • Transaction-Related Data:

      • Volume (V)

      • Open Interest (OI)

      • Market-Breadth Data:

      • Advances (A)

      • Declines (D)

      • Total Issues (T)

      • Up Volume/Down Volume (UV, DV)

      • New Highs/New Lows (NH, NL)

      • Bullish Percent Data

      • Sentiment Data:

      • Put/Call Ratio

      • Short Interest Ratio

      • Specialist/Public Ratio

      • Cash/Asset Ratio

      • Investor and Advisor Poll Data

      • Margin Debt

      • Implied Volatility (VIX)

      The majority of charts are simple price-time charts, using the basic open, high, low, and close information, popularly referred to as OHLC data. Let us now turn our attention to how OHLC data is created.

       Quantization of Price

In order to create OHLC data, we first need to specify the time interval over which price activity occurs. For example, let us assume that we are interested in identifying the opening, high, low, and closing prices over five-minute intervals. We therefore need to separate or quantize price activity with respect to time, for each successive 5-min interval, or period. An interval or period may be of any duration, but the most popular intervals are 1-min, 5-min, 15-min, 1-hour, 4-hour, daily, weekly, monthly, and yearly. In our example, the price at the beginning and end of any 5-min interval represents the Opening (O) and Closing (C) price respectively, while the highest and lowest price within that period represents the High (H) and Low (L) prices. In most cases, the closing price will also represent the opening price of the next 5-min interval, unless there is a gap in price. Figure 3.1 depicts price activity within a 5-min period. Price is quantized into 5-min intervals and is summarized into four pieces of information, namely the OHLC data. Note that OHLC data is also used to construct other representations of price activity like bar charts, Gann bars, and Japanese candlesticks.

Figure 3.1 Filtering Price Action into Four Pieces of Information (OHLC).

      The range of a bar or candlestick is simply the absolute difference between the high and low price, that is, range = |H – L |.

Refer to Figure 3.2. To create OHLC data for bars and candlesticks over longer periods, simply identify the:

      1. Opening price of the first period (O)

      2. Closing price of the last period (C)

      3. Highest price between the opening and closing price (H)

      4. Lowest price between the opening and closing price (L)

Figure 3.2 Higher Timeframe Price