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:

- Average Directional Index (ADX)
- Average True Range (ATR)
- Bollinger Bands
- Exponential Moving Average (EMA)
- Full Stochastics
- Keltner Channels
- MACD
- Moving Average Envelopes
- Price Channels
- Rate of Change
- Relative Strength Index (RSI)
- Simple Moving Average (SMA)
- Volatility
- Wilder's Moving Average

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

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:

- Greater than 40: strong trend
- Below 20: weak trend
- Move from below 20 to above 20: possible end of a ranging period and start of a trend
- Move from above 40 to below 40: early sign of a weakening trend
- +DI move above -DI: basic buy signal
- -DI move above +DI: basic sell signal

To determine the ADX we need a few related indicators:

- Upward Move (+DM)
- Downward Move (-DM)
- Positive Directional Indicator (+DI)
- Negative Directional Indicator (-DI)
- Directional Index (DX)

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) 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:

- The difference between its current High and Low
- The [abs] difference between its current High and previous Close
- The [abs] difference between its current Low and previous Close

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 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:

- Width of the band: An indicator of the overall volatility
- Projection of price targets: Breaking above or below the central SMA line may signal the upper or the lower band - respectively - as the next price target
- Trend indication: Price breakouts from the upper or lower band may indicate continuation of the current trend

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:

- The current trend gaining momentum
- Loss of momentum and reversal of trend

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:

- Central line: The 20-day Simple Moving Average.
- Upper band: SMA(20) + 2xStandard Deviation of 20-day Close Price
- Lower band: SMA(20) - 2xStandard Deviation of 20-day Close Price

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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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:

- 20-day
- 50-day
- 100-day
- 200-day

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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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
- %D
- %D-slow

%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:

- In an uptrend securities tend to trade at the top of their price range
- In a downtrend securities tend to trade at the bottom of their price range
- Securities at the top of their price range are under a buy pressure
- Securities at the bottom of their price range are under a sell pressure
- Fast Stochastics
- %K-fast is highly sensitive to price changes
- %D-fast is a smoothed version - usually a 3-day SMA - of %K-fast and serves as a trigger line
- %K-fast crossovers of %D-fast are analyzed as buy or sell signals
- Due to their highly responsive nature, Fast Stochastics tend to capture many of the undesirable price whipsaws
- Slow Stochastics
- Developed to avoid premature entering and exiting of positions associated with Fast Stochastics
- In Slow Stochastics %D-fast - described above - serves as %K-slow
- %D-slow is calculated as a 3-day SMA of %K-slow
- Trading signals are considered to have occurred when:
- %D-slow crosses high bands in the range of 75 to 80% OR
- %D-slow crosses low bands in the range of 15 to 20% AND
- %K-slow crosses the %D-slow trigger line

- Full Stochastics
- Takes into account three parameters. At Flookii we are using the following:
- %K-full = 14-day %K-fast
- %D-fast = 5-day SMA of %K-fast, used as the signal line
- %D-full = 3-day SMA of %D line

- Flookii provides two reports based on Full Stochastics:
- Overbought Crossovers
- %D-fast > 75% AND
- %D-fast < %D-full
- Oversold Crossovers
- %D-fast < 25% AND
- %D-fast > %D-full

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.

- The original center line in Keltner Channels was a 10-day Simple Moving Average (SMA). Some traders prefered the speed of the 10-day moving average. However, at Flookii we use the modern version of Keltner Channels introduced by Linda Raschke, which is built around a 20-day Exponential Moving Average (EMA)
- Upper band: 20-day EMA + 2x10-day Average True Range (ATR)
- Lower band: 20-day EMA - 2x10-day ATR

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:

- Penetration of the upper band: Buy signal
- Penetration of the lower band: Sell signal

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

- Most of the price movement occurs within the channel
- Upper and lower limits of the channel reflect extremes of price movement
- Price exceeds upper band: Sell signal
- Price exceeds lower band: Buy signal

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:

- MACD penetration above its 9-day EMA: Bullish crossover
- MACD penetration below its 9-day EMA: Bearish crossover
- Positive MACD divergence from closing price: upward momentum
- Negative MACD divergence from closing price: downward momentum

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 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:

- Centerline: 20-day SMA
- Quick-response upper band: 20-day SMA + (0.03x20-day SMA)
- Quick-response lower band: 20-day SMA - (0.03x20-day SMA)
- Slow-response upper band: 20-day SMA + (0.06x20-day SMA)
- Slow-response lower band: 20-day SMA - (0.06x20-day SMA)

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

- Price penetration above the SMA line may indicate a bullish trend
- Price penetration below the SMA line may indicate a bearish trend

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:

- Price penetrates above the upper band: Buy signal
- Price penetrates below the lower band: Sell signal

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

- Upper band: Plotted along the highest high of n days from previous close
- Lower band: Plotted along the lowest low of n days from previous close

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

- Price penetration of the upper band: Bullish trend
- Price penetration of the lower band: Bearish trend

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

Where:

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:

- ROC rising above 0: Short-term bullish signal
- ROC rising below 0: Short-term bearish signal

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:

- RSI > 75: Overbought
- RSI < 25: Oversold

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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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:

- Simple Moving Average (SMA) is an unweighted average. Therefore each closing price of the previous n days has the same influence on the overall calculation
- Exponential Moving Average (EMA) is a weighted average; the more recent data has more impact than older data in the range

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:

- 20-day
- 50-day
- 100-day
- 200-day

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:

- Close price rises above 3x20-day ATR: Buy signal
- Close price drops below 3x20-day ATR: Sell signal

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:

- Displays less lag in reflecting the price move than SMA
- Represents a smoother moving average than EMA

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:

- The difference between two prices, such as (Close(i)-Close(i-n))
- The difference between a price and its moving average
- The difference between two moving averages

Common momentum indicators include:

- Rate of Change (ROC)
- Relative Strength Index (RSI)
- Stochastics
- Moving Average Convergence Divergence (MACD)

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:

- Rising prices cross above the trendline: Buy signal
- Falling prices cross below the trendline: Sell signal

Some of the common trend analysis systems include:

- Simple Moving Average (SMA) Crossovers
- Exponential Moving Average (EMA) Crossovers
- Moving Average Envelopes (MA) Breakouts
- Bollinger Bands Breakouts
- Keltner Channels Breakouts
- Price Channels Breakouts
- Volitility Breakouts

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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