Moving average
In statistics, a moving average or rolling average is one of a family of
similar techniques used to analyze time series data. It is applied in
finance and especially in technical analysis. It can also be used as a
generic smoothing operation, in which case the raw data need not be a time
series.
A moving average series can be calculated for any time series. In finance
it is most often applied to stock prices, returns or trading volumes.
Moving averages are used to smooth out shortterm fluctuations, thus
highlighting longerterm trends or cycles. The threshold between
shortterm and longterm depends on the application, and the parameters of
the moving average will be set accordingly.
Mathematically, each of these moving averages is an example of a
convolution. These averages are also similar to the lowpass filters used
in signal processing.
Simple moving average
Prior moving average
A simple moving average (SMA) is the unweighted mean of the previous n
data points. For example, a 10day simple moving average of closing price
is the mean of the previous 10 days' closing prices.
When calculating successive values, a new value comes into the sum and an
old value drops out, meaning a full summation each time is unnecessary,
In technical analysis there are various popular values for n, like 10
days, 40 days, or 200 days. The period selected depends on the kind of
movement one is concentrating on, such as short, intermediate, or long
term. In any case moving average levels are interpreted as support in a
rising market, or resistance in a falling market.
In all cases a moving average lags behind the latest data point, simply
from the nature of its smoothing. An SMA can lag to an undesirable extent,
and can be disproportionately influenced by old data points dropping out
of the average. This is addressed by giving extra weight to more recent
data points, as in the weighted and exponential moving averages.
One characteristic of the SMA is that if the data has a periodic
fluctuation, then applying an SMA of that period will eliminate that
variation (the average always containing one complete cycle). But a
perfectly regular cycle is rarely encountered in economics or finance.
Central moving average For a number of applications it is advantageous to avoid the shifting
induced by using only 'past' data. Hence a central moving average can be
computed, using both 'past' and 'future' data. The 'future' data in this
case are not predictions, but merely data obtained after the time at which
the average is to be computed.
Weighted and exponential moving averages (see below) can also be computed
centrally. Weighted moving average A weighted average is any average that has multiplying factors to give
different weights to different data points. But in technical analysis a
weighted moving average (WMA) has the specific meaning of weights which
decrease arithmetically. In an nday WMA the latest day has weight n, the
second latest n1, etc, down to zero.
Exponential moving average
An exponential moving average (EMA),
sometimes also called an exponentially weighted moving average (EWMA), applies weighting factors
which decrease exponentially. The weighting for each older data point
decreases exponentially, giving much more importance to recent
observations while still not discarding older observations entirely.
The degree of weighing decrease is expressed as a constant smoothing
factor Multiplier, a number between 0 and 1. Multiplier may be expressed as a percentage, so
a smoothing factor of 10% is equivalent to Multiplier=0.1. Alternatively,
Multiplier may be
expressed in terms of N time periods, where . For example, N=19 is
equivalent to Multiplier=0.1.
Exponential Moving Averages can be specified in two ways  as a
percentbased EMA or as a periodbased EMA. A percentbased EMA has a
percentage as it's single parameter while a periodbased EMA has a
parameter that represents the duration of the EMA.
The formula for an exponential moving average is:
EMA(current) = ( (Price(current) 
EMA(prev) ) x Multiplier) + EMA(prev)
For a percentagebased EMA, "Multiplier" is equal to the EMA's specified
percentage. For a periodbased EMA, "Multiplier" is equal to 2 / (1 + N)
where N is the specified number of periods.
For example, a 10period EMA's Multiplier is calculated like this:
(2 / (Time periods + 1) ) = (2 / (10
+ 1) ) = 0.1818 (18.18%)
This means that a 10period EMA is equivalent to an 18.18% EMA.

Accumulation Distribution
Bollinger Bands
Commodity Channel Index
Momentum
Money Flow
Moving Average
On Balance Volume
Parabolic SAR
Relative Strength Index
Stochastic oscillator
Support and Resistance
The Elliott wave principle
Trend Lines
Triple Exponential Average
Volume At Price
