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Types Of Indicators

Types Of Indicators

The indicators and line studies found in MetaStock can be divided into six categories. The intent of this guide is to help you develop better trading systems. A robust technical trading system should probably incorporate indicators from several of these categories. Note that some indicators fall into more than one category.

 

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Cycle Indicators

The following indicators and line studies can be used to measure cycles. Many securities, particularly futures, show a tendency to move in cyclical patterns. Price changes can often be anticipated at key cyclical intervals.

 Indicators Creator Reference
 
Cycle Lines
Detrended Price Oscillator
Fibonacci Time Zones
Fourier Transform
MESA Sine Wave Indicator
 
 
 
 
 
Ehlers
 
Cycle Lines
Detrended Price Oscillator
Time Zones
Fourier Transform
MESA Sine Wave


Market Strength Indicators

The following indicators can be used to measure market strength. Each of these indicators incorporate either volume or open interest, which are the basic ingredients to the measurement of market strength. Generally higher volume and/or open interest levels indicate more participants and therefore more strength

The following indicators can be used to measure market strength. Each of these indicators incorporate either volume or open interest, which are the basic ingredients to the measurement of market strength. Generally higher volume and/or open interest levels indicate more participants and therefore more strength.

 Indicators Creator Reference
 
Accumulation/Distribution
Demand Index
Chaikin Money Flow
Chaikin Oscillator
Ease of Movement
 
Williams
Sibbet
Chaikin
Chaikin
Arms
 
Accumulation/Distribution
Demand Index
Chaikin Money Flow
Chaikin A/D Oscillator
Ease of Movement
 
Herrick Payoff Index
Klinger Oscillator
Money Flow Index
Moving Average (VMA)
Negative Volume Index
 
Herrick
Klinger
Quong/Soudack
Arms
Fosback
 
Herrick Payoff Index
Klinger Oscillator
Money Flow Index
Moving Average Calculation Methods
Negative Volume Index
 
On Balance Volume
Open Interest
Positive Volume Index
Price Volume Trend
Trade Volume Index
Volume
Volume Oscillator
Volume Rate-Of-Change
 
Granville
 
Fosback
 
Slauson
 
 
 
 
On Balance Volume
Open Interest
Positive Volume Index
Price Volume Trend
Trade Volume Index
Volume
Volume Oscillator
Volume Rate-Of-Change


Momentum Indicators

The following indicators can be used to measure momentum. Momentum is a general term used to describe the speed at which prices move over a given time period. Generally, changes in momentum tend to lead changes in prices.

 Indicators Creator Reference
 
Accumulation Swing Index
Chande Momentum Oscillator
Commodity Channel Index
Dynamic Momentum Index
Intraday Momentum Index
Linear Regression Slope
MACD
 
Wilder
Chande
Lambert
Chande
Chande
 
Appel
 
Accumulation Swing Index
Chande Momentum Oscillator
Commodity Channel Index
Dynamic Momentum Index
Intraday Momentum Index
Linear Regression Slope
MACD
 
Mass Index
Momentum Indicator
Price Oscillator
Price Rate-Of-Change
Random Walk Index
Range Indicator
Relative Momentum Index
Relative Strength Index
 
Dorsey
 
 
Appel
Poulus
Weinberg
Altman
Wilder
 
Mass Index
Momentum
Price Oscillator
Price Rate-Of-Change
Random Walk Index
Range Indicator
Relative Momentum Index
Relative Strength Index
 
Stochastic Momentum Index
Stochastic Oscillator
Swing Index
TRIX
Ultimate Oscillator
Williams' %R
Williams' Accum/Distribution
 
Blau
Lane
Wilder
 
Williams
Williams
Williams
 
Stochastic Momentum Index
Stochastic Oscillator
Swing Index
TRIX
Ultimate Oscillator
Williams' %R
Williams' Accum/Distribution


Support and Resistance Indicators

The following indicators and line studies can be used to measure support and resistance. A common occurrence is for prices to repeatedly rise or fall to a certain level and then reverse. This phenomenon (attributed to basic supply and demand) is called support and resistance.

 Indicators Creator Reference
 
Andrews' Pitchfork
Envelope
Fibonacci Arcs, Fans, Retracements
Gann Lines, Fans, Grids
Projection Bands
Projection Oscillator
Quadrant Lines
Speed Resistance Lines
Tirone Levels
Trendlines
 
Andrews
 
 
Gann
Widner
Widner
 
 
Tirone
 
 
Andrews' Pitchfork
Envelope
Fibonacci Studies
Gann Studies
Projection Bands
Projection Oscillator
Quadrant Lines
Speed Resistance Lines
Tirone Levels
Trendlines


Trend Indicators

The following indicators and line studies can be used to measure trend. Trend is a term used to describe the persistence of prices to move in one direction.

 Indicators Creator
 
Aroon
Commodity Selection Index
DEMA
Directional Movement
Forecast Oscillator
Linear Regression Indicator
Linear Regression Slope
Linear Regression Trendline
MACD
Moving Averages 
(all calculation methods)
Parabolic SAR
Performance
Polarized Fractal Efficiency
Price Oscillator
Qstick Indicator
r-squared
Raff Regression Channel
Standard Deviation Channel 
Standard Error
Standard Error Bands
Standard Error Channel 
TEMA
Time Series Forecast
Trendlines
Vertical Horizontal Filter
Zig Zag
 
Chande
Wilder
Mulloy
Wilder
Chande
   
   
   
Appel
 
   
Wilder
   
Hannula
   
Chande
   
Raff
Equis
   
Andersen
Equis
Mulloy
   
   
White
Merril


Volatility Indicators

The following indicators can be used to measure volatility. Volatility is a general term used to describe the magnitude of day-to-day fluctuations in prices (independent of direction). Generally, changes in volatility tend to lead changes in prices.

 Indicators Creator Reference
 
Average True Range
Bollinger Bands
Commodity Selection Index
Moving Average (variable)
ODDS™ Probability Cones
Relative Volatility Index
Standard Deviation
Standard Error Bands
Volatility, Chaikin's
Volatility, Option
 
Wilder
Bollinger
Wilder
Chande
Fishback
Dorsey
 
Andersen
Chaikin
Bookstaber
 
Average True Range
Bollinger Bands
Commodity Selection Index
Variable
ODDS Probability Cones 
Relative Volatility Index
Standard Deviation
Standard Error Bands
Volatility, Chaikin's
Option Volatility


Statistical Tools

Autoregressive Integrated Moving Average - ARIMA

A statistical analysis model that uses time series data to predict future trends. It is a form of regression analysis that seeks to predict future movements along the seemingly random walk taken by stocks and the financial market by examining the differences between values in the series instead of using the actual data values. Lags of the differenced series are referred to as "Autoregressive" and lags within forecasted data are referred to as "Moving Average."
 
This model type is generally referred to as ARIMA(p,d,q), with the integers referring to the autoregressive, integrated and moving average parts of the data set, respectively. ARIMA modeling can take into account trends, seasonality, cycles, errors and non-stationary aspects of a data set when making forecasts.
 
 


Generalized AutoRegressive Conditional Heteroskedasticity - (GARCH)

A statistical model used by financial institutions to estimate the volatility of stock returns. This information is used by banks to help determine what stocks will potentially provide higher returns, as well as to forecast the returns of current investments to help in the budgeting process.
 
There are many variations of GARCH, including NGARCH to include correlation, and IGARCH which restricts the volatility parameter. Each model can be used to accomodate the specific qualities of the stock, industry or economic state.

Recent Blog Entries

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Inflation is bad for bonds, eroding their value as prices and yields, unless index-linked, fail to keep pace with rising prices.

Inflation drives interest rates higher and bond prices down. In terms of inflation adjusted returns, bonds have actually been more risky than equities for most of the nineteenth and twentieth centuries.

However, many believe that bonds may outperform stocks over the next few years as stocks’ recent strong performance makes them less attractive and as deflation becomes a more potent force than inflation.

Financial engineering is, in essence, the phenomenon of innovation in the financial industries: securities innovation; innovative financial processes; and creative solutions to corporate finance problems.

The biggest challenges over the next few decades for financial engineering will focus on aspects of human judgment that are now considered impossible to mimic computationally: fear, greed and other emotional aspects of decision-making.

Recent advances in the cognitive sciences, neurobiology and computer science may provide some clues to solving these tantalizing problems in financial contexts.

One of the most high profile features of the business and investment worlds is corporate restructuring — mergers and acquisitions (M&A), leveraged buyouts, divestitures, spinoffs and the like.

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The primary argument in favor of M&A is that they are good for industrial efficiency. Takeovers are a radical solution for remedying poor performance and safeguarding against economic mediocrity.

The essence of quantitative investing is crunching numbers. Anything that can go into a digital computer is fair input. And since computers are mostly digital and linear programs are rigid, “quant” analysis tends to be repetitively structured and rich in reliance on back-testing.

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Going long” an investment means buying it in the expectation of a future price rise. “Going short” is the opposite: selling something you do not own in the hope of buying it back more cheaply in the future.

When you go long, your loss is limited to what you paid for the stock. But when you go short, your losses are potentially without limit as the buyback price may rise ever higher above the price at which you sold. Why sell short?

The obvious answer is to profit from the impending decline of an overpriced stock. But shorting requires a tough-minded pessimism, a contrarian turn of mind or a “gloom and doom” view of the world.
Bears don'’t live on Park Avenue
Bernard Baruch, 1870-1965, American financier and statesman,adviser to
presidents and popular sage