Knowledge Center

What is Technical Analysis?

In a nutshell technical analysis is a method aimed at predicting future trends and price movements of a security based on its past and current performance.

The method uses mathematical formulas to transform previous price movements into models of market behavior. Comparison of current price movements against these models allows the technical analyst to identify bearish / bullish trends or repeating tradable patterns.

Critics of technical analysis oppose the possibility of predicting future market behavior; they mainly rely on fundamental analysis of securities. Many traders however argue that a stock's price movement reflects its underlying fundamentals as well as overall market attitude. To these traders technical analysis serves as an indicator that signals possible trading opportunities.

At Flookii we believe both methods hold their own merit. However, our main focus is on the following popular technical analysis measures:

In addition to the above indicators, some common technical analysis terms include:


Average Directional Index (ADX)

Formulated by J. Welles Wilder Jr., Average Directional Index or ADX measures the strength of a trend. It is measured on a scale of 0 to 100, although readings greater than 60 are rare.

Most common interpretations of ADX and its underlying indicators include:

To determine the ADX we need a few related indicators:

To calculate +DI and -DI we need to first capture the up and down moves, which are measured by considering the difference between current and previous high, low and close prices.

When the difference between current high and yesterday's high is greater than the difference between yesterday's low and current low:

+DM = current high - yesterday's high; otherwise +DM = 0

Similarly when the difference between yesterday's low and current low is greater than the difference between current high and yesterday's high:

-DM = yesterday's low - current low; otherwise -DM = 0

To determine +DI and -DI, the Wilder's moving average of +DM and -DM is divided by Average True Range.

The Directional Index (DX) is calculated as the difference between +DI and -DI divided by the sum:

DX = [+DI]-[-DI] / [+DI]+[-DI]

ADX = (14-day Wilder moving average) of DX

ADX does not reflect the direction of a trend. In addition to ADX(14) Flookii reports display "ADX Trend(14)" where we compare current +DI and -DI to the same indicators 14 days ago in order to ascertain a general direction.

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Average True Range (ATR)

Average True Range (ATR) is an indicator of volatility developed by J. Welles Wilder, Jr. ATR is calculated as the average of a security's daily price range smoothed over a period of 'n' days.

A security's daily price range or True Range (TR)

is the greatest of the following:

ATR is commonly calculated over a 14-day period by applying the Wilder's Moving Average to the TR.

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Bollinger Bands

Bollinger Bands are a set of high and low lines around a Simple Moving Average central line. Developed by John Bollinger, these bands provide a measure of price volatility.

Interpretations of Bollinger Bands include:

At Flookii we provide reports of initial 20-day high and low breakouts/break-ins. These initial reports can be used to observe the possibility of:

Flookii's Bollinger Bands reports do not provide buy or sell signals. Information provided in these reports must be analyzed in combination with other indicators such as the Relative Strength Index (RSI).

To determine the upper and lower Bollinger Bands we calculate:

By applying the standard deviation to the SMA line, we create a band that adapts itself to market volatility; hence the bands widen in more volatile periods and become narrower during quieter times.

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Exponential Moving Average (EMA)

In statistics the rolling average of a subset of data points within a larger data set is known as the moving average; see Simple Moving Average (SMA).

In technical analysis SMAs are commonly used to smooth out short-term volatility. However, one side effect of this smoothing is the generation of a lag between actual trend and its representation in SMA graphs.

Exponential Moving Averages are weighted averages with emphasis on the more recent activity. This weighted calculation reduces the lag that commonly accompanies SMAs.

At Flookii we provide EMA crossover reports for the following periods:

While the above indicators help identify a trend, whipsaw crossovers are quite common. Therefore it is prudent to confirm the trend using additional indicators.

To calculate the EMA we use the following formula:

EMA = ((current close-previous EMA) x (2/(n+1))) + previous EMA

Where n = number of days in the period

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Full Stochastics

Formulated by George Lane, the Stochastic oscillator compares the position of the closing price of a security to its high/low range within a period. The comparison is used to identify momentum.

The Stochastic measurement yields three indicators:

%K, also referred to as %K-fast or Fast Stochastic measures the ratio of the difference of current close and lowest low within the period to the maximum price range of the same period. %K fluctuates between 0 and 100.

%K = 100 x ((current close - low(n))/(high(n) - low(n)))

Where low(n) and high(n) are the lowest low and the highest high for a period of n days.

%D, also called %D-fast is an n-day simple moving average (SMA) of %K.

%D-slow is an n-day SMA of %D-fast.

Technical analysts refer to three types of Stochastics: Fast, Slow and full. The assumptions associated with Stochastics are:

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Keltner Channels

Keltner Channels as the name indicates consist of a channel of upper and lower bands placed at a distance from a center line of a moving average.

Keltner Channels were originally described as the 10-day Moving Average Rule by Chester W. Keltner. In his book Trading Systems and Methods, Perry J. Kaufmann provides the following interpretation for Keltner Channels:

Other interpretations of Keltner Channels are related to the general concept of channel breakouts:

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Moving Average Convergence Divergence (MACD)

MACD is one of the most widely used momentum indicators. Developed by Gerald Appel, the MACD consists of an oscillator around a centerline.

The oscillator is calculated using a fast and a slow exponential moving average (EMA) of the closing price:

MACD = 12-day EMA - 26-day EMA

The centerline, which serves as a signal line is a smoothed version of MACD:

Signal line = 9-day EMA of MACD

The difference between MACD and its 9-day EMA signal line creates the oscillator.

While the MACD can reflect actual closing prices and their respective EMAs, at Flookii we calculate it in percentages; this enables comparisons between different securities.

Common interpretations of MACD include:

Positive divergence is identified when the closing price hits a new low but MACD fails to drop to a new low itself. Conversely a negative divergence occurs when the closing price reaches a new high but MACD does not follow suit.

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Moving Average (MA) Envelopes

Moving Average envelopes consist of a channel created around a simple moving average (SMA) centerline. The channel is calculated as a fixed percentage of the SMA. At Flookii we use:

The concept of MA envelope breakouts is similar to SMA crossovers but with one main difference. In SMA crossovers:

However, whipsaws around the SMA line are quite common and can result in frequent false signals.

In MA envelopes the fixed-percentage channel created around the SMA line serves as a buffer zone. This buffer zone helps filter out some of the false signals related to whipsaws seen in SMA crossovers.

As with other channel breakouts, the general interpretation of MA envelope breakouts includes the following:

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Price Channels

Similar to other channels and bands, Price Channels consist of an upper and lower band around the price line.

Interpretation of Price Channel breakouts is similar to that of other channel breakouts:

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Rate of Change (ROC)

Rate of Change (ROC) measures the percentage of change of a security's price from one period to another. ROC is the simplest representation of a price's velocity of change, and therefore one of the most widely used momentum indicators

ROC is calculated as:

((Close(i) - Close(i-n)) / Close(i-n)) * 100


Close(i) = Current Closing Price
Close(i-n) = Closing Price n-days ago

ROC is generally measured for a 10-day time frame. However, Flookii reports display a 14-day ROC.

Rate of Change is represented as an oscillator. Its most common interpretation includes:

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Relative Strength Index (RSI)

Relative Strength Index (RSI) compares a security's average gain compared to its average loss within a defined period. Developed by J. Welles Wilder Jr., RSI is a widely used momentum indicator, that oscillates between 0 and 100.

At Flookii we calculate a 14-day RSI, which involves the following:

Relative Strength (RS) = Average Gain/Average Loss

Where the 14-day Average Gain and Loss are calculated using
Wilder's moving average:

Average Gain = ((Previous Average Gain*13)+Current Gain)/14
Average Loss = ((Prev. Avg. Loss*13)+Abs(Current Loss))/14

RSI = 100-(100/(1+RS))

Interpretations of RSI include:

Flookii reports display the current RSI(14). In addition we compare the current RSI to RSI of 14 days ago in order to ascertain a general direction.

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Simple Moving Average (SMA)

A moving average is the rolling average of a subset of data points within a larger data set.

Moving averages can be calculated in several different ways. Two types are most widely used in technical analysis; both are averages of previous n-day period closing prices. However:

Both SMA and EMA are used as trendlines. However the unweighted nature of SMA results in a more lagging representation of price activity than EMA.

Due to its ability to smooth out some of the short-term volatility Long-term traders may prefer to use SMA as a trend indicator.

At Flookii we provide the following SMA crossover reports:

Moving average crossovers may be interpreted as trend signals. Additional indicators should be used to confirm the trend and define trade entry/exit points.

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Volatility refers to the fluctuations in a security's price within a defined time period. These fluctuations represent the relative, underlying risk associated with that security.

Different measures of volatility are common including Standard Deviation and Beta Coefficient. At Flookii we use Average True Range (ATR).

Some short-term trader/technical analysts use Volatility breakouts to determine entry/exit points. A trend is believed to have initiated when a security's price moves more than a certain distance from its average range. Volatility breakout systems aim at capturing this price move.

The general approach to Volatility breakouts is:

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Wilder's Moving Average (WMA)

J. Welles Wilder's Moving Average is a weighted moving average. However, the weighting differs from the typical calculation of an Exponential Moving Average (EMA).

For a period of n days, WMA of the closing price is calculated as:

(Current Close Price+(Previous WMAx(n-1)))/n

Wilder's Moving Average vs. SMA and EMA:

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Momentum refers to the velocity of change in a security's price. Technical analysts typically trade the trend. Identifying trend reversals as early as possible is therefore of particular importance. Momentum indicators can be used to confirm trend reversals.

A popular interpretation of momentum is to identify high and low extremes, which reflect overbought and oversold territory. Analysts believe that the market cannot sustain the strength of the trend in such extremes. In fact, some technical traders use momentum on the premise that it precedes the price move. These traders generally look for lower lows or higher highs in momentum to spot an immediate trend.

Momentum can refer to:

Common momentum indicators include:

Short-term traders value the use of momentum indicators, especially for identifying potential opportunity for swing trades.

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Trend refers to the dominant direction of a security's price within a period of time. However, price movements do not always translate into actual trends. Technical analysts use a variety of trend indicators to help them distinguish between actual price direction and erroneous fluctuations due to volatility.

In order to identify trend, we need to determine the relationship between time and price. The analysis of the correlation of paired series of data - in this case Time/Price series - is known as Regression Analysis.

Linear Regression is the most commonly used regression model. Linear Regression attempts to identify a 'best-fit' line that crosses through the majority of data points within a plotted paired series.

Market trend, however, can rarely be forced into a straight line. More commonly trend can be represented with statistical curves that reflect peaks and troughs of price movement. The non-linear approximation can be achieved using another type of Regression Analysis known as an Autoregressive model.

Autoregression attempts to use data to create a forecasting model for the same data. In the case of market analysis this refers to using past prices of a security to forecast its future price.

Any forecasting model is associated with variable degrees of forecast error. Moving averages, which smooth the data, can be used to reduce forecast error. The combination of autoregression and moving averages is known as an Autoregressive Integrated Moving Average (ARIMA) model.

Smoothing the trend creates a lag, which regardless of the direction, puts the trendline behind the price movement. So in upward markets the trendline is below the price and in downward markets the trendline is above the price. Also when the market changes direction, the trendline is slow to reflect the change.

The response lag between the trendline and the actual price movement helps identify entry/exit signals. It is generally accepted that:

Some of the common trend analysis systems include:

In addition to the systems listed above, among technical analysts, J. Welles Wilder Jr.'s Average Directional Index (ADX) is a widely accepted trend indicator.

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